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DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL

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Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL

#91

Can CoT models also call external functions? What if they had access to a calculator?

Or a code interpreter in the chain of thought. I think then it really gets quite close to how humans go about solving problems.

What about both? Or say a set of standard tools a modern intelligent agent[0] should have some proficiency in. A calculator, a basic code interpreter for a single high-level language, a graphing tool[1], web search, database search. And then maybe a tool for managing its own context[2]. How far could we get with a dataset designed specifically to train the model in pure tool use? That is, one that assumes the model never actually knows the answer to a question (even if the base model does), and instead trains it to aggressively use tools to break the problem down into steps[3] - steps that are primarily more tool calls, to query external sources, process information, simulate, etc. until the answer is computed. No direct answers, just tool calls glued by thinking in terms of tool calls, or thinking by tool calls.

I wonder if this has been tried. It probably has, seeing how hot this area of research is today. If anyone knows of a paper or a dataset, I'd appreciate a link.

Anyway, I wonder what would happen if we tried it with this method - basically retraining the model to trust its own toolbox - or as some would say, "shut up and multiply" - and do it across all tasks, not strictly math or coding ones.

--

[0] - Digital or otherwise.

[1] - Or the one tool that does all three, and which most people older than ~25 y.o. likely used at least once in their lives: Microsoft Excel. Or any other spreadsheet app. Though for LLMs as they are now, I suppose code interpreter would be a better unifying paradigm due to being 1D instead of 2D.

[2] - E.g. changeNotesAndRethink("text", 0, 1) -> replace current output with "text", continue generation; changeNotesAndRethink("text", -1, 2) -> replace fixed "assistant notes prompt" with "text" and discard last two outputs[4] and continue, etc. Honestly, I'm surprised I haven't seen it done so far - not in the popular places I know, at least (vendor apps, TypingMind, ComfyUI); I've heard of some attempts long ago (back when LangChain was still seen as hot). Did giving the model control over the chat loop never pan out? Or is there some fundamental reason this doesn't work?

[3] - I may have accidentally done this in-context with Claude 3.5 Sonnet - if I prompt it for chain-of-thought and happen to have Mermaid Diagram plugin enabled in TypingMind, it almost always ends up producing multiple diagrams as part of the CoT phase. Notably, this doesn't happen with my own equivalent plugin (PlantUML), so I wonder if it's just something about that specific tool, or if "thinking with (Mermaid) diagrams" was part of the training set.

EDIT:

[4] - APIs for tool-using models seem to allow several LLM outputs in a row. But that makes me think (and I apologize for this post being almost all footnotes, but ideas just keep coming) - what about rewinding back past one or more user messages in a multi-turn conversation, while retaining them? Like "Fill in the Middle" mode[5], just over entire conversation instead of a single message?

[5] - OpenAI used to have that, right now I think only DeepSeek does - https://api-docs.deepseek.com/api/create-completion.

Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL

#92
post #10

Does anyone have any good simple prompts for testing new "reasoning" models like this one? "Count the letter Rs in the word strawberry" is a bit dull! I'm trying this one locally using Ollama and the smallest quantized GGUF version (769MB) I could find - https://huggingface.co/bartowski/agentica-org_DeepScaleR-1.5... - I ran it like this: ollama run hf.co/bartowski/agentica-org_DeepScaleR-1.5B-Preview-GGUF:IQ3_XXS He…

I have two. One is a simple one that only deepseek R1 has passed (in my opinion): I have a 12 liter jug and a 6 liter jug. How do I get exactly 6 liters of water? Answer (Deepseek): Fill the 6-liter jug completely to obtain exactly 6 liters of water. Every other LLM I've tried include o3-mini-high: Fill the 12-liter jug completely. Pour it into the 6 liter jug. Although o3 did get it right in the reasoning: It seems…

And things get dramatically worse on 4o if you add a second superfluous jug:

    To measure exactly 6 liters of water using a 16-liter jug, a 12-liter jug, and a 6-liter jug, follow these steps:
    Step-by-Step Solution:

        Fill the 12-liter jug completely (it now holds 12 liters).
        Pour water from the 12-liter jug into the 16-liter jug. The 16-liter jug now contains 12 liters, and the 12-liter jug is empty.
        Fill the 12-liter jug completely again (it now holds 12 liters).
        Pour water from the 12-liter jug into the 16-liter jug until the 16-liter jug is full. Since the 16-liter jug already has 12 liters, it can only take 4 more liters. This leaves 8 liters in the 12-liter jug.
        Empty the 16-liter jug completely.
        Pour the remaining 8 liters from the 12-liter jug into the 16-liter jug.
        Fill the 12-liter jug completely again (it now holds 12 liters).
        Pour water from the 12-liter jug into the 16-liter jug until the 16-liter jug contains exactly 16 liters. Since the 16-liter jug already has 8 liters, it can only take 8 more liters, leaving 4 liters in the 12-liter jug.
        Pour the 4 liters from the 12-liter jug into the empty 6-liter jug. The 6-liter jug now contains 4 liters.
        Fill the 12-liter jug completely again (it now holds 12 liters).
        Pour water from the 12-liter jug into the 6-liter jug until the 6-liter jug is full. Since the 6-liter jug already has 4 liters, it can only take 2 more liters. This leaves 10 liters in the 12-liter jug.
        Empty the 6-liter jug completely.
        Pour the remaining 10 liters from the 12-liter jug into the 6-liter jug.

    Now, the 6-liter jug contains exactly 6 liters of water.

Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL

#93

Earlier quoted context omitted.

I have two. One is a simple one that only deepseek R1 has passed (in my opinion): I have a 12 liter jug and a 6 liter jug. How do I get exactly 6 liters of water? Answer (Deepseek): Fill the 6-liter jug completely to obtain exactly 6 liters of water. Every other LLM I've tried include o3-mini-high: Fill the 12-liter jug completely. Pour it into the 6 liter jug. Although o3 did get it right in the reasoning: It seems…

>Every other LLM I've tried include o3-mini-high: Fill the 12-liter jug completely. Pour it into the 6 liter jug. Try it with a 12L jug and a 4L jug and ask for 4L. See if it tells you to just fill the 4L or to fill the 12L and pour into the 4L twice discarding both times and there will be 4L remaining in the 12L jug. Even though it's still technically correct, it demonstrates that there's no real "reasoning" happeni…

(Shrug) R1 has no problem with that. To the extent it's confused, it is only because it is wondering if it's a trick question.

CoT reasoning: https://i.imgur.com/rjNmTGZ.png

Answer: https://i.imgur.com/WfAVeZQ.png

There's 'mindless regurgitation' going on here, but not by the AI model.

Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL

#94
post #78

Earlier quoted context omitted.

I’m not sure about that, I think the current setup is an artifact of human limits, which an immortal shared ML model doesn’t share. Also, cross-disciplinary knowledge is frequently incredibly helpful (if rare, because currently it’s the intersection of multiple uncommon specialties).

The question that's screaming at me right now is that if humans used specialization + cooperation to achieve global dominance, what happens when you pair unlimited global masterminds + cooperation?

Best case scenario is you get The Culture where society is ruled by hyper-intelligent but kind and benevolent AIs that solve all economic problems and that keep a population of quadrillions of very happy, healthy, and rich humans as pets.

Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL

#95
post #88

Earlier quoted context omitted.

The question that's screaming at me right now is that if humans used specialization + cooperation to achieve global dominance, what happens when you pair unlimited global masterminds + cooperation?

We turn into cats.

I like optimists.

Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL

#96
post #35
post #18

This is finetuned to the benchmarks and nowhere close to O1-Preview in any other tasks. Not worth looking into unless you specifically want to solve these problems - however, still impressive.

We beat O1-preview and even many other 7B models over many math benchmarks, which was TEST set (not in training set at all). If you want to make the model fully generalist, feel free to train it over coding datasets (such as RL with passing unit tests as reward).

It is great discovery, it could even open a next step in AI with MoM "Mixture of Models", where small fine-tuned models take each part of a task (instead of the current MoE)

Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL

#97

Earlier quoted context omitted.

I have two. One is a simple one that only deepseek R1 has passed (in my opinion): I have a 12 liter jug and a 6 liter jug. How do I get exactly 6 liters of water? Answer (Deepseek): Fill the 6-liter jug completely to obtain exactly 6 liters of water. Every other LLM I've tried include o3-mini-high: Fill the 12-liter jug completely. Pour it into the 6 liter jug. Although o3 did get it right in the reasoning: It seems…

Wow, Gemini 2 has a 9 step process for the first question https://sugaku.net/qna/1d922e55-fd28-4c70-b3e9-d70c3a05ce1f/ > Here's how you can get exactly 6 liters of water using a 12-liter jug and a 6-liter jug: 1. Fill the 6-liter jug completely. 2. Pour the water from the 6-liter jug into the 12-liter jug. You now have 6 liters in the 12-liter jug. 3. Fill the 6-liter jug completely again. 4. Pour water from the 6-li…

It really loves the sound of its own voice!

Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL

#98
post #51

The key thing here is a simple, reliable formula to train a 1B model on a specific task and get strong performance. That didn’t really exist before. Edge devices are about to get a lot smarter.

Still took $4500, so I'll probably not training this at home - but maybe that number will change too! Exciting times.

Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL

#99
post #96
post #35

Earlier quoted context omitted.

We beat O1-preview and even many other 7B models over many math benchmarks, which was TEST set (not in training set at all). If you want to make the model fully generalist, feel free to train it over coding datasets (such as RL with passing unit tests as reward).

It is great discovery, it could even open a next step in AI with MoM "Mixture of Models", where small fine-tuned models take each part of a task (instead of the current MoE)

Check out one of my prior work: https://stylus-diffusion.github.io/

This work scales up selection/routing over many models/LoRAs

Re: DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL

#100
post #99
post #96

Earlier quoted context omitted.

It is great discovery, it could even open a next step in AI with MoM "Mixture of Models", where small fine-tuned models take each part of a task (instead of the current MoE)

Check out one of my prior work: https://stylus-diffusion.github.io/ This work scales up selection/routing over many models/LoRAs

Love it, will check, thank you for showing / sharing all of that!
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