Prediction

Model

pipelines-beta/pdf-to-podcast

ab33w9ns35rma0cpxqmvdfj3n4

Status

Succeeded

Source

Web

Hardware

CPU (Small)

Total duration

85.0s

Created

6 months ago by pipelines-beta

Webhook

–

Input

pdf: https://replicate.delivery/pbxt/N2eLwcjeFEn7TGji7Rwrupayhic5F4s9PbQLFBLwkbAMFy3Q/2505.00024v2.pdf
host_name: Adam
guest_name: Bella
host_voice: Patient_Man
guest_voice: Wise_Woman
duration_minutes: 5
podcast_topic: Empty string
monologue: false

{
  "duration_minutes": 5,
  "guest_name": "Bella",
  "guest_voice": "Wise_Woman",
  "host_name": "Adam",
  "host_voice": "Patient_Man",
  "monologue": false,
  "pdf": "https://replicate.delivery/pbxt/N2eLwcjeFEn7TGji7Rwrupayhic5F4s9PbQLFBLwkbAMFy3Q/2505.00024v2.pdf",
  "podcast_topic": ""
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_Y5B**********************************

This is your API token. Keep it to yourself.

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run pipelines-beta/pdf-to-podcast using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const input = {
  duration_minutes: 5,
  guest_name: "Bella",
  guest_voice: "Wise_Woman",
  host_name: "Adam",
  host_voice: "Patient_Man",
  monologue: false,
  pdf: "https://replicate.delivery/pbxt/N2eLwcjeFEn7TGji7Rwrupayhic5F4s9PbQLFBLwkbAMFy3Q/2505.00024v2.pdf",
  podcast_topic: ""
};

const output = await replicate.run("pipelines-beta/pdf-to-podcast", { input });

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_Y5B**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run pipelines-beta/pdf-to-podcast using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pipelines-beta/pdf-to-podcast",
    input={
        "duration_minutes": 5,
        "guest_name": "Bella",
        "guest_voice": "Wise_Woman",
        "host_name": "Adam",
        "host_voice": "Patient_Man",
        "monologue": False,
        "pdf": "https://replicate.delivery/pbxt/N2eLwcjeFEn7TGji7Rwrupayhic5F4s9PbQLFBLwkbAMFy3Q/2505.00024v2.pdf",
        "podcast_topic": ""
    }
)

# To access the file URL:
print(output.url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output.read())

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_Y5B**********************************

This is your API token. Keep it to yourself.

Run pipelines-beta/pdf-to-podcast using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "input": {
      "duration_minutes": 5,
      "guest_name": "Bella",
      "guest_voice": "Wise_Woman",
      "host_name": "Adam",
      "host_voice": "Patient_Man",
      "monologue": false,
      "pdf": "https://replicate.delivery/pbxt/N2eLwcjeFEn7TGji7Rwrupayhic5F4s9PbQLFBLwkbAMFy3Q/2505.00024v2.pdf",
      "podcast_topic": ""
    }
  }' \
  https://api.replicate.com/v1/models/pipelines-beta/pdf-to-podcast/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "id": "ab33w9ns35rma0cpxqmvdfj3n4",
  "model": "pipelines-beta/pdf-to-podcast",
  "version": "hidden",
  "input": {
    "duration_minutes": 5,
    "guest_name": "Bella",
    "guest_voice": "Wise_Woman",
    "host_name": "Adam",
    "host_voice": "Patient_Man",
    "monologue": false,
    "pdf": "https://replicate.delivery/pbxt/N2eLwcjeFEn7TGji7Rwrupayhic5F4s9PbQLFBLwkbAMFy3Q/2505.00024v2.pdf",
    "podcast_topic": ""
  },
  "logs": "TTS completed, combining audio files\n2025-05-20 13:37:20 [info     ] [main] fast loader failed: name 'VOICES' is not defined\n2025-05-20 13:37:20 [debug    ] [main] falling back to slow loader\nProcessing PDF 1/1: tmp7ik8p19z2505.00024v2.pdf\n<<< PDF summary >>>\n# Summary of Nemotron-Research-Tool-N1\nThe paper introduces Nemotron-Research-Tool-N1 (Tool-N1), a series of language models designed to enhance tool-calling capabilities through reinforcement learning (RL) rather than traditional supervised fine-tuning (SFT). Unlike previous approaches that rely on imitating reasoning trajectories from stronger models, Tool-N1 uses a binary RL reward system that only evaluates the format validity and functional correctness of tool invocations, without supervising the intermediate reasoning process.\nThe research demonstrates that Tool-N1-7B/14B models outperform GPT-4o on several major benchmarks. Through a systematic study using 5,518 distilled reasoning trajectories, the authors compare SFT, RL, and combined SFT-then-RL pipelines, finding that pure RL can be more effective than the widely adopted SFT-then-RL approach for tool-calling tasks. This challenges conventional wisdom about the necessity of supervised pre-training before reinforcement learning.\nThe approach offers several advantages over traditional methods: it provides interpretable supervision signals through rule-based reward design; it enables more flexible training by not enforcing strict output matching; and it allows models to develop their own reasoning strategies independently without relying on annotated trajectories. This work contributes to advancing tool-using language models by demonstrating how reinforcement learning can be effectively applied to develop stronger reasoning capabilities in tool-calling scenarios.\n<<< Podcast outline >>>\n# Podcast Outline: \"AI Breakthroughs: Reinforcement Learning for Tool-Calling Models\"\n## Introduction (30 seconds)\n- **Adam**: Welcomes listeners to the podcast, introduces the topic of tool-calling AI models\n- **Adam**: Introduces guest Bella, an AI researcher specializing in language models\n- **Adam**: Sets up the conversation about the Nemotron-Research-Tool-N1 paper and its significance\n## Segment 1: Understanding Tool-N1 and Its Innovation (60 seconds)\n- **Adam**: Asks Bella to explain what Tool-N1 is and how it differs from previous models\n- **Bella**: Explains:\n- Tool-N1 is a series of language models focused on tool-calling\n- The key innovation: using reinforcement learning (RL) instead of supervised fine-tuning\n- Models come in 7B and 14B parameter sizes\n- **Adam**: Follows up on why tool-calling capabilities matter in modern AI\n## Segment 2: The RL Approach vs. Traditional Methods (60 seconds)\n- **Adam**: Asks about the traditional approach to training tool-using models\n- **Bella**: Contrasts approaches:\n- Traditional: Models imitate reasoning trajectories from stronger models (SFT)\n- Tool-N1 approach: Binary RL reward system based on format validity and functional correctness\n- No supervision of intermediate reasoning steps\n- **Adam**: Expresses surprise that the model doesn't need guidance on reasoning steps\n## Segment 3: Performance and Benchmark Results (45 seconds)\n- **Adam**: Inquires about performance compared to industry standards\n- **Bella**: Shares key results:\n- Tool-N1-7B/14B outperforms GPT-4o on several major benchmarks\n- Discusses the significance of smaller models outperforming larger ones\n- **Adam**: Asks what specific benchmarks were most impressive\n## Segment 4: Challenging Conventional Wisdom (45 seconds)\n- **Adam**: Discusses the conventional SFT-then-RL pipeline\n- **Bella**: Explains the study findings:\n- 5,518 distilled reasoning trajectories were analyzed\n- Pure RL approach proved more effective than the standard SFT-then-RL pipeline\n- This challenges long-held beliefs about needing supervised pre-training\n- **Adam**: Remarks on how this finding might reshape AI training approaches\n## Segment 5: Advantages of the Tool-N1 Approach (60 seconds)\n- **Adam**: Asks about practical benefits of this approach\n- **Bella**: Outlines three key advantages:\n- Interpretable supervision through rule-based reward design\n- More flexible training without enforcing strict output matching\n- Models develop independent reasoning strategies\n- **Adam**: Explores how this might impact future AI systems and development costs\n## Conclusion (30 seconds)\n- **Adam**: Asks Bella about implications for the future of AI tool-using capabilities\n- **Bella**: Gives brief thoughts on future research directions and applications\n- **Adam**: Thanks Bella for sharing insights and summarizes key takeaways for listeners\n- **Adam**: Closes the podcast with encouragement for listeners to explore more on the topic# Podcast Outline: \"Breaking Paradigms in AI Tool Development\"\n*Host: Adam | Guest: Dr. Bella Chen, AI Researcher*\n## Segment 1: Introduction (30 seconds)\n- **Adam:** Welcomes listeners and introduces the topic of revolutionary approaches in AI tool-calling models\n- **Adam:** Introduces Dr. Bella Chen, lead researcher on the Nemotron-Research-Tool-N1 project\n- **Bella:** Brief greeting and contextualizes the importance of tool-calling capabilities in modern AI systems\n## Segment 2: The Traditional vs. New Approach (60 seconds)\n- **Adam:** Asks Bella to explain the conventional wisdom in training tool-using AI models\n- **Bella:** Outlines the traditional SFT-then-RL pipeline that most researchers follow\n- **Bella:** Introduces Tool-N1's departure from conventional methods by using pure reinforcement learning\n- **Adam:** Clarifies for listeners what \"tool-calling\" means in practical terms with examples\n## Segment 3: The Binary Reward System (45 seconds)\n- **Adam:** Questions how Tool-N1's approach differs technically from previous models\n- **Bella:** Explains the binary reward system that only assesses format validity and functional correctness\n- **Bella:** Highlights how this differs from imitating reasoning trajectories from larger models\n- **Adam:** Asks about the benefits of not supervising the intermediate reasoning process\n## Segment 4: Performance Breakthrough (45 seconds)\n- **Adam:** Requests details on Tool-N1's performance compared to industry leaders\n- **Bella:** Shares findings that Tool-N1-7B/14B outperforms GPT-4o on major benchmarks\n- **Bella:** Explains significance of achieving superior results with smaller models\n- **Adam:** Asks about the study methodology involving 5,518 distilled reasoning trajectories\n## Segment 5: Challenging Conventional Wisdom (60 seconds)\n- **Adam:** Explores the counterintuitive finding that pure RL can outperform SFT-then-RL\n- **Bella:** Discusses why this challenges established beliefs in the field\n- **Bella:** Explains potential reasons why RL alone works better for tool-calling tasks\n- **Adam:** Questions whether this finding applies to other AI capabilities beyond tool-calling\n## Segment 6: Key Advantages of the Approach (45 seconds)\n- **Adam:** Asks about practical benefits of Tool-N1's methodology\n- **Bella:** Outlines three major advantages:\n1. Interpretable supervision signals through rule-based reward design\n2. More flexible training without enforcing strict output matching\n3. Models developing independent reasoning strategies\n- **Adam:** Explores how these benefits translate to real-world applications\n## Segment 7: Future Implications (30 seconds)\n- **Adam:** Questions how this research might influence future AI development\n- **Bella:** Discusses potential paradigm shifts in training methodologies\n- **Bella:** Speculates on how these findings might be applied to other AI capabilities\n- **Adam:** Asks about next steps for the Tool-N1 research team\n## Segment 8: Conclusion and Sign-off (15 seconds)\n- **Adam:** Summarizes key takeaways about Tool-N1 and its novel approach\n- **Adam:** Thanks Bella for her insights\n- **Bella:** Final thoughts on the future of tool-using AI models\n- **Adam:** Directs listeners to resources for learning more and closes the episode\n<<< Podcast content >>>\n{\"title\": \"AI Breakthroughs: Reinforcement Learning Revolutionizes Tool-Calling Models\", \"summary\": \"Adam and AI researcher Bella discuss the groundbreaking Nemotron-Research-Tool-N1 paper, which challenges conventional wisdom by using pure reinforcement learning instead of supervised fine-tuning for tool-calling AI models. They explore how this approach achieves superior performance with smaller models and what it means for the future of AI development.\", \"lines\": [\n{\"text\": \"Welcome to 'Tech Horizons,' where we explore the cutting edge of artificial intelligence. I'm your host, Adam. Today, we're diving into a fascinating breakthrough in tool-calling AI models. Joining me is Bella, an AI researcher specializing in language models and part of the team behind the Nemotron-Research-Tool-N1 paper. Bella, thanks for being here!\", \"speaker\": \"Adam\"},\n{\"text\": \"Thanks for having me, Adam. Excited to chat about our work on Tool-N1.\", \"speaker\": \"Bella\"},\n{\"text\": \"So let's start with the basics. What exactly is Tool-N1, and how does it differ from previous models in this space?\", \"speaker\": \"Adam\"},\n{\"text\": \"Tool-N1 is essentially a series of language models specifically designed for tool-calling capabilities. The key innovation here is our approach to training. While most models in this space rely on supervised fine-tuning, where they essentially imitate reasoning trajectories from stronger models, we took a different path using reinforcement learning or RL. We've developed versions in both 7 billion and 14 billion parameter sizes.\", \"speaker\": \"Bella\"},\n{\"text\": \"For our listeners who might not be familiar, could you explain what 'tool-calling' actually means in the context of AI, and why it matters?\", \"speaker\": \"Adam\"},\n{\"text\": \"Sure thing. Tool-calling is an AI's ability to recognize when it needs external capabilities and then correctly invoke them. Think of an AI that knows when to use a calculator, search an API, or access a database. It's crucial because it extends what AI can do beyond its training data, making it much more useful in real-world applications.\", \"speaker\": \"Bella\"},\n{\"text\": \"That makes sense. Now, you mentioned using reinforcement learning rather than supervised fine-tuning. Could you break down the traditional approach versus what you did with Tool-N1?\", \"speaker\": \"Adam\"},\n{\"text\": \"Traditionally, we'd train a model by showing it examples of correct reasoning trajectories from stronger models – essentially saying 'copy this expert.' With Tool-N1, we implemented a binary reward system that only evaluates two things: is the format valid, and is the function call correct? We don't supervise any of the intermediate reasoning steps. The model figures those out entirely on its own.\", \"speaker\": \"Bella\"},\n{\"text\": \"Wait, so you're not telling the model how to think through the problem? That seems counterintuitive. I would have thought you'd want to guide its reasoning process.\", \"speaker\": \"Adam\"},\n{\"text\": \"That's exactly what makes this interesting! The conventional wisdom has always been that you need to guide models through complex reasoning. Our research shows that with the right reward structure, the model can develop its own effective reasoning strategies. It's like teaching a child by only telling them if the final answer is right, not how to solve the problem.\", \"speaker\": \"Bella\"},\n{\"text\": \"That's fascinating. So how does Tool-N1 perform compared to industry standards like GPT-4?\", \"speaker\": \"Adam\"},\n{\"text\": \"The results actually surprised us. Both our Tool-N1-7B and 14B models outperform GPT-4o on several major benchmarks. That's remarkable considering our models are much smaller. The 7B model has less than 1% of the parameters that GPT-4 likely has, yet it still outperforms it on tool-calling tasks.\", \"speaker\": \"Bella\"},\n{\"text\": \"Which benchmarks were most impressive?\", \"speaker\": \"Adam\"},\n{\"text\": \"We tested across a range of tool-based reasoning tasks. What's particularly exciting is our performance on complex multi-step reasoning problems where the model needs to chain several tool calls together to reach a solution. These are precisely the kinds of tasks where you'd expect supervised fine-tuning to be necessary.\", \"speaker\": \"Bella\"},\n{\"text\": \"You know, there's been this standard pipeline in AI development where you do supervised fine-tuning first, then apply reinforcement learning. But your paper seems to challenge that approach?\", \"speaker\": \"Adam\"},\n{\"text\": \"Absolutely. We systematically analyzed 5,518 distilled reasoning trajectories comparing SFT, RL, and combined SFT-then-RL pipelines. What we found is that pure RL can actually be more effective than the standard SFT-then-RL pipeline for tool-calling tasks. This really does challenge the conventional wisdom that you need supervised pre-training before applying reinforcement learning.\", \"speaker\": \"Bella\"},\n{\"text\": \"That could have huge implications for how AI systems are developed. What would you say are the practical advantages of your approach?\", \"speaker\": \"Adam\"},\n{\"text\": \"There are three key advantages. First, we get interpretable supervision through rule-based reward design – we know exactly what we're reinforcing. Second, the training is more flexible since we're not enforcing strict output matching. And third, models develop independent reasoning strategies rather than just imitating larger models. This could lead to more diverse and potentially more innovative AI systems.\", \"speaker\": \"Bella\"},\n{\"text\": \"And potentially more cost-effective development, right? If smaller models can perform at this level without the complex supervised fine-tuning step?\", \"speaker\": \"Adam\"},\n{\"text\": \"That's a great point. The computational resources needed for traditional supervised approaches can be enormous. Our method potentially offers a more efficient path to developing highly capable AI systems, which could democratize access to this technology.\", \"speaker\": \"Bella\"},\n{\"text\": \"As we wrap up, what do you see as the future implications of this work for AI tool-using capabilities?\", \"speaker\": \"Adam\"},\n{\"text\": \"I think we're just scratching the surface. This approach could extend beyond tool-calling to other capabilities requiring complex reasoning. It suggests we might need to rethink some fundamental assumptions about how to train AI systems. Rather than more supervision, sometimes the right incentive structure might be all you need for AI to develop sophisticated capabilities.\", \"speaker\": \"Bella\"},\n{\"text\": \"Fascinating stuff. Thank you, Bella, for sharing these insights on Tool-N1. To our listeners, this conversation highlights how reinforcement learning alone might be more powerful than we thought for developing sophisticated AI capabilities. It's another reminder of how quickly this field is evolving. Thanks for tuning in to 'Tech Horizons,' and we'll catch you next time.\", \"speaker\": \"Adam\"}\nffmpeg version 5.1.6-0+deb12u1 Copyright (c) 2000-2024 the FFmpeg developers\nbuilt with gcc 12 (Debian 12.2.0-14)\nconfiguration: --prefix=/usr --extra-version=0+deb12u1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libdav1d --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libglslang --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librabbitmq --enable-librist --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzimg --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --disable-sndio --enable-libjxl --enable-pocketsphinx --enable-librsvg --enable-libmfx --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libx264 --enable-libplacebo --enable-librav1e --enable-shared\nlibavutil      57. 28.100 / 57. 28.100\nlibavcodec     59. 37.100 / 59. 37.100\nlibavformat    59. 27.100 / 59. 27.100\nlibavdevice    59.  7.100 / 59.  7.100\nlibavfilter     8. 44.100 /  8. 44.100\nlibswscale      6.  7.100 /  6.  7.100\nlibswresample   4.  7.100 /  4.  7.100\nlibpostproc    56.  6.100 / 56.  6.100\n[mp3 @ 0x56977b85fe00] Estimating duration from bitrate, this may be inaccurate\nInput #0, concat, from '/tmp/file_list.txt':\n  Duration: N/A, start: 0.000000, bitrate: 128 kb/s\n  Stream #0:0: Audio: mp3, 32000 Hz, mono, fltp, 128 kb/s\nOutput #0, mp3, to 'podcast.mp3':\nMetadata:\n    TSSE            : Lavf59.27.100\n  Stream #0:0: Audio: mp3, 32000 Hz, mono, fltp, 128 kb/s\nStream mapping:\n  Stream #0:0 -> #0:0 (copy)\nPress [q] to stop, [?] for help\nsize=       1kB time=00:00:00.03 bitrate= 266.0kbits/s speed=N/A\n[mp3 @ 0x56977b879500] Estimating duration from bitrate, this may be inaccurate\n[mp3 @ 0x56977b8824c0] Estimating duration from bitrate, this may be inaccurate\n[mp3 @ 0x56977b875940] Estimating duration from bitrate, this may be inaccurate\n[mp3 @ 0x56977b86ae80] Estimating duration from bitrate, this may be inaccurate\n[mp3 @ 0x56977b864240] Estimating duration from bitrate, this may be inaccurate\n[mp3 @ 0x56977b863880] Estimating duration from bitrate, this may be inaccurate\n[mp3 @ 0x56977b8a6c80] Estimating duration from bitrate, this may be inaccurate\nLast message repeated 2 times\n[mp3 @ 0x56977b863880] Estimating duration from bitrate, this may be inaccurate\nsize=    6711kB time=00:07:09.48 bitrate= 128.0kbits/s speed=7.94e+03x\nvideo:0kB audio:6711kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.009037%\n]}",
  "output": "https://replicate.delivery/xezq/LFsu42ESPqZeAiDrpPuKDlWYxksRM4ZTrzjX2BLklfkkfGdpA/podcast.mp3",
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2025-05-20T13:37:19.641Z",
  "started_at": "2025-05-20T13:37:19.739452Z",
  "completed_at": "2025-05-20T13:38:44.647769Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/ab33w9ns35rma0cpxqmvdfj3n4/cancel",
    "children": "https://api.replicate.com/v1/predictions/ab33w9ns35rma0cpxqmvdfj3n4/children",
    "get": "https://api.replicate.com/v1/predictions/ab33w9ns35rma0cpxqmvdfj3n4",
    "root": "https://api.replicate.com/v1/predictions/ab33w9ns35rma0cpxqmvdfj3n4",
    "stream": "https://stream.replicate.com/v1/files/bcwr-nrjyvpknijblbumijbe2eqtzltkrwpkh2b5udroukqvniqzh2eha",
    "web": "https://replicate.com/p/ab33w9ns35rma0cpxqmvdfj3n4"
  },
  "metrics": {
    "predict_time": 84.908316711,
    "total_time": 85.006769
  }
}

Generated in

84.9 seconds

Tweak it Report

TTS completed, combining audio files 2025-05-20 13:37:20 [info ] [main] fast loader failed: name 'VOICES' is not defined 2025-05-20 13:37:20 [debug ] [main] falling back to slow loader Processing PDF 1/1: tmp7ik8p19z2505.00024v2.pdf <<< PDF summary >>> # Summary of Nemotron-Research-Tool-N1 The paper introduces Nemotron-Research-Tool-N1 (Tool-N1), a series of language models designed to enhance tool-calling capabilities through reinforcement learning (RL) rather than traditional supervised fine-tuning (SFT). Unlike previous approaches that rely on imitating reasoning trajectories from stronger models, Tool-N1 uses a binary RL reward system that only evaluates the format validity and functional correctness of tool invocations, without supervising the intermediate reasoning process. The research demonstrates that Tool-N1-7B/14B models outperform GPT-4o on several major benchmarks. Through a systematic study using 5,518 distilled reasoning trajectories, the authors compare SFT, RL, and combined SFT-then-RL pipelines, finding that pure RL can be more effective than the widely adopted SFT-then-RL approach for tool-calling tasks. This challenges conventional wisdom about the necessity of supervised pre-training before reinforcement learning. The approach offers several advantages over traditional methods: it provides interpretable supervision signals through rule-based reward design; it enables more flexible training by not enforcing strict output matching; and it allows models to develop their own reasoning strategies independently without relying on annotated trajectories. This work contributes to advancing tool-using language models by demonstrating how reinforcement learning can be effectively applied to develop stronger reasoning capabilities in tool-calling scenarios. <<< Podcast outline >>> # Podcast Outline: "AI Breakthroughs: Reinforcement Learning for Tool-Calling Models" ## Introduction (30 seconds) - **Adam**: Welcomes listeners to the podcast, introduces the topic of tool-calling AI models - **Adam**: Introduces guest Bella, an AI researcher specializing in language models - **Adam**: Sets up the conversation about the Nemotron-Research-Tool-N1 paper and its significance ## Segment 1: Understanding Tool-N1 and Its Innovation (60 seconds) - **Adam**: Asks Bella to explain what Tool-N1 is and how it differs from previous models - **Bella**: Explains: - Tool-N1 is a series of language models focused on tool-calling - The key innovation: using reinforcement learning (RL) instead of supervised fine-tuning - Models come in 7B and 14B parameter sizes - **Adam**: Follows up on why tool-calling capabilities matter in modern AI ## Segment 2: The RL Approach vs. Traditional Methods (60 seconds) - **Adam**: Asks about the traditional approach to training tool-using models - **Bella**: Contrasts approaches: - Traditional: Models imitate reasoning trajectories from stronger models (SFT) - Tool-N1 approach: Binary RL reward system based on format validity and functional correctness - No supervision of intermediate reasoning steps - **Adam**: Expresses surprise that the model doesn't need guidance on reasoning steps ## Segment 3: Performance and Benchmark Results (45 seconds) - **Adam**: Inquires about performance compared to industry standards - **Bella**: Shares key results: - Tool-N1-7B/14B outperforms GPT-4o on several major benchmarks - Discusses the significance of smaller models outperforming larger ones - **Adam**: Asks what specific benchmarks were most impressive ## Segment 4: Challenging Conventional Wisdom (45 seconds) - **Adam**: Discusses the conventional SFT-then-RL pipeline - **Bella**: Explains the study findings: - 5,518 distilled reasoning trajectories were analyzed - Pure RL approach proved more effective than the standard SFT-then-RL pipeline - This challenges long-held beliefs about needing supervised pre-training - **Adam**: Remarks on how this finding might reshape AI training approaches ## Segment 5: Advantages of the Tool-N1 Approach (60 seconds) - **Adam**: Asks about practical benefits of this approach - **Bella**: Outlines three key advantages: - Interpretable supervision through rule-based reward design - More flexible training without enforcing strict output matching - Models develop independent reasoning strategies - **Adam**: Explores how this might impact future AI systems and development costs ## Conclusion (30 seconds) - **Adam**: Asks Bella about implications for the future of AI tool-using capabilities - **Bella**: Gives brief thoughts on future research directions and applications - **Adam**: Thanks Bella for sharing insights and summarizes key takeaways for listeners - **Adam**: Closes the podcast with encouragement for listeners to explore more on the topic# Podcast Outline: "Breaking Paradigms in AI Tool Development" *Host: Adam | Guest: Dr. Bella Chen, AI Researcher* ## Segment 1: Introduction (30 seconds) - **Adam:** Welcomes listeners and introduces the topic of revolutionary approaches in AI tool-calling models - **Adam:** Introduces Dr. Bella Chen, lead researcher on the Nemotron-Research-Tool-N1 project - **Bella:** Brief greeting and contextualizes the importance of tool-calling capabilities in modern AI systems ## Segment 2: The Traditional vs. New Approach (60 seconds) - **Adam:** Asks Bella to explain the conventional wisdom in training tool-using AI models - **Bella:** Outlines the traditional SFT-then-RL pipeline that most researchers follow - **Bella:** Introduces Tool-N1's departure from conventional methods by using pure reinforcement learning - **Adam:** Clarifies for listeners what "tool-calling" means in practical terms with examples ## Segment 3: The Binary Reward System (45 seconds) - **Adam:** Questions how Tool-N1's approach differs technically from previous models - **Bella:** Explains the binary reward system that only assesses format validity and functional correctness - **Bella:** Highlights how this differs from imitating reasoning trajectories from larger models - **Adam:** Asks about the benefits of not supervising the intermediate reasoning process ## Segment 4: Performance Breakthrough (45 seconds) - **Adam:** Requests details on Tool-N1's performance compared to industry leaders - **Bella:** Shares findings that Tool-N1-7B/14B outperforms GPT-4o on major benchmarks - **Bella:** Explains significance of achieving superior results with smaller models - **Adam:** Asks about the study methodology involving 5,518 distilled reasoning trajectories ## Segment 5: Challenging Conventional Wisdom (60 seconds) - **Adam:** Explores the counterintuitive finding that pure RL can outperform SFT-then-RL - **Bella:** Discusses why this challenges established beliefs in the field - **Bella:** Explains potential reasons why RL alone works better for tool-calling tasks - **Adam:** Questions whether this finding applies to other AI capabilities beyond tool-calling ## Segment 6: Key Advantages of the Approach (45 seconds) - **Adam:** Asks about practical benefits of Tool-N1's methodology - **Bella:** Outlines three major advantages: 1. Interpretable supervision signals through rule-based reward design 2. More flexible training without enforcing strict output matching 3. Models developing independent reasoning strategies - **Adam:** Explores how these benefits translate to real-world applications ## Segment 7: Future Implications (30 seconds) - **Adam:** Questions how this research might influence future AI development - **Bella:** Discusses potential paradigm shifts in training methodologies - **Bella:** Speculates on how these findings might be applied to other AI capabilities - **Adam:** Asks about next steps for the Tool-N1 research team ## Segment 8: Conclusion and Sign-off (15 seconds) - **Adam:** Summarizes key takeaways about Tool-N1 and its novel approach - **Adam:** Thanks Bella for her insights - **Bella:** Final thoughts on the future of tool-using AI models - **Adam:** Directs listeners to resources for learning more and closes the episode <<< Podcast content >>> {"title": "AI Breakthroughs: Reinforcement Learning Revolutionizes Tool-Calling Models", "summary": "Adam and AI researcher Bella discuss the groundbreaking Nemotron-Research-Tool-N1 paper, which challenges conventional wisdom by using pure reinforcement learning instead of supervised fine-tuning for tool-calling AI models. They explore how this approach achieves superior performance with smaller models and what it means for the future of AI development.", "lines": [ {"text": "Welcome to 'Tech Horizons,' where we explore the cutting edge of artificial intelligence. I'm your host, Adam. Today, we're diving into a fascinating breakthrough in tool-calling AI models. Joining me is Bella, an AI researcher specializing in language models and part of the team behind the Nemotron-Research-Tool-N1 paper. Bella, thanks for being here!", "speaker": "Adam"}, {"text": "Thanks for having me, Adam. Excited to chat about our work on Tool-N1.", "speaker": "Bella"}, {"text": "So let's start with the basics. What exactly is Tool-N1, and how does it differ from previous models in this space?", "speaker": "Adam"}, {"text": "Tool-N1 is essentially a series of language models specifically designed for tool-calling capabilities. The key innovation here is our approach to training. While most models in this space rely on supervised fine-tuning, where they essentially imitate reasoning trajectories from stronger models, we took a different path using reinforcement learning or RL. We've developed versions in both 7 billion and 14 billion parameter sizes.", "speaker": "Bella"}, {"text": "For our listeners who might not be familiar, could you explain what 'tool-calling' actually means in the context of AI, and why it matters?", "speaker": "Adam"}, {"text": "Sure thing. Tool-calling is an AI's ability to recognize when it needs external capabilities and then correctly invoke them. Think of an AI that knows when to use a calculator, search an API, or access a database. It's crucial because it extends what AI can do beyond its training data, making it much more useful in real-world applications.", "speaker": "Bella"}, {"text": "That makes sense. Now, you mentioned using reinforcement learning rather than supervised fine-tuning. Could you break down the traditional approach versus what you did with Tool-N1?", "speaker": "Adam"}, {"text": "Traditionally, we'd train a model by showing it examples of correct reasoning trajectories from stronger models – essentially saying 'copy this expert.' With Tool-N1, we implemented a binary reward system that only evaluates two things: is the format valid, and is the function call correct? We don't supervise any of the intermediate reasoning steps. The model figures those out entirely on its own.", "speaker": "Bella"}, {"text": "Wait, so you're not telling the model how to think through the problem? That seems counterintuitive. I would have thought you'd want to guide its reasoning process.", "speaker": "Adam"}, {"text": "That's exactly what makes this interesting! The conventional wisdom has always been that you need to guide models through complex reasoning. Our research shows that with the right reward structure, the model can develop its own effective reasoning strategies. It's like teaching a child by only telling them if the final answer is right, not how to solve the problem.", "speaker": "Bella"}, {"text": "That's fascinating. So how does Tool-N1 perform compared to industry standards like GPT-4?", "speaker": "Adam"}, {"text": "The results actually surprised us. Both our Tool-N1-7B and 14B models outperform GPT-4o on several major benchmarks. That's remarkable considering our models are much smaller. The 7B model has less than 1% of the parameters that GPT-4 likely has, yet it still outperforms it on tool-calling tasks.", "speaker": "Bella"}, {"text": "Which benchmarks were most impressive?", "speaker": "Adam"}, {"text": "We tested across a range of tool-based reasoning tasks. What's particularly exciting is our performance on complex multi-step reasoning problems where the model needs to chain several tool calls together to reach a solution. These are precisely the kinds of tasks where you'd expect supervised fine-tuning to be necessary.", "speaker": "Bella"}, {"text": "You know, there's been this standard pipeline in AI development where you do supervised fine-tuning first, then apply reinforcement learning. But your paper seems to challenge that approach?", "speaker": "Adam"}, {"text": "Absolutely. We systematically analyzed 5,518 distilled reasoning trajectories comparing SFT, RL, and combined SFT-then-RL pipelines. What we found is that pure RL can actually be more effective than the standard SFT-then-RL pipeline for tool-calling tasks. This really does challenge the conventional wisdom that you need supervised pre-training before applying reinforcement learning.", "speaker": "Bella"}, {"text": "That could have huge implications for how AI systems are developed. What would you say are the practical advantages of your approach?", "speaker": "Adam"}, {"text": "There are three key advantages. First, we get interpretable supervision through rule-based reward design – we know exactly what we're reinforcing. Second, the training is more flexible since we're not enforcing strict output matching. And third, models develop independent reasoning strategies rather than just imitating larger models. This could lead to more diverse and potentially more innovative AI systems.", "speaker": "Bella"}, {"text": "And potentially more cost-effective development, right? If smaller models can perform at this level without the complex supervised fine-tuning step?", "speaker": "Adam"}, {"text": "That's a great point. The computational resources needed for traditional supervised approaches can be enormous. Our method potentially offers a more efficient path to developing highly capable AI systems, which could democratize access to this technology.", "speaker": "Bella"}, {"text": "As we wrap up, what do you see as the future implications of this work for AI tool-using capabilities?", "speaker": "Adam"}, {"text": "I think we're just scratching the surface. This approach could extend beyond tool-calling to other capabilities requiring complex reasoning. It suggests we might need to rethink some fundamental assumptions about how to train AI systems. Rather than more supervision, sometimes the right incentive structure might be all you need for AI to develop sophisticated capabilities.", "speaker": "Bella"}, {"text": "Fascinating stuff. Thank you, Bella, for sharing these insights on Tool-N1. To our listeners, this conversation highlights how reinforcement learning alone might be more powerful than we thought for developing sophisticated AI capabilities. It's another reminder of how quickly this field is evolving. Thanks for tuning in to 'Tech Horizons,' and we'll catch you next time.", "speaker": "Adam"} ffmpeg version 5.1.6-0+deb12u1 Copyright (c) 2000-2024 the FFmpeg developers built with gcc 12 (Debian 12.2.0-14) configuration: --prefix=/usr --extra-version=0+deb12u1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libdav1d --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libglslang --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librabbitmq --enable-librist --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzimg --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --disable-sndio --enable-libjxl --enable-pocketsphinx --enable-librsvg --enable-libmfx --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libx264 --enable-libplacebo --enable-librav1e --enable-shared libavutil 57. 28.100 / 57. 28.100 libavcodec 59. 37.100 / 59. 37.100 libavformat 59. 27.100 / 59. 27.100 libavdevice 59. 7.100 / 59. 7.100 libavfilter 8. 44.100 / 8. 44.100 libswscale 6. 7.100 / 6. 7.100 libswresample 4. 7.100 / 4. 7.100 libpostproc 56. 6.100 / 56. 6.100 [mp3 @ 0x56977b85fe00] Estimating duration from bitrate, this may be inaccurate Input #0, concat, from '/tmp/file_list.txt': Duration: N/A, start: 0.000000, bitrate: 128 kb/s Stream #0:0: Audio: mp3, 32000 Hz, mono, fltp, 128 kb/s Output #0, mp3, to 'podcast.mp3': Metadata: TSSE : Lavf59.27.100 Stream #0:0: Audio: mp3, 32000 Hz, mono, fltp, 128 kb/s Stream mapping: Stream #0:0 -> #0:0 (copy) Press [q] to stop, [?] for help size= 1kB time=00:00:00.03 bitrate= 266.0kbits/s speed=N/A [mp3 @ 0x56977b879500] Estimating duration from bitrate, this may be inaccurate [mp3 @ 0x56977b8824c0] Estimating duration from bitrate, this may be inaccurate [mp3 @ 0x56977b875940] Estimating duration from bitrate, this may be inaccurate [mp3 @ 0x56977b86ae80] Estimating duration from bitrate, this may be inaccurate [mp3 @ 0x56977b864240] Estimating duration from bitrate, this may be inaccurate [mp3 @ 0x56977b863880] Estimating duration from bitrate, this may be inaccurate [mp3 @ 0x56977b8a6c80] Estimating duration from bitrate, this may be inaccurate Last message repeated 2 times [mp3 @ 0x56977b863880] Estimating duration from bitrate, this may be inaccurate size= 6711kB time=00:07:09.48 bitrate= 128.0kbits/s speed=7.94e+03x video:0kB audio:6711kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.009037% ]}