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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

#121

Post by viraptor »

pona-a wrote: post #104
gpjanik wrote: 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.

I tested it on basic long addition problems. It frequently misplaced the decimal signs, used unnecessary reasoning tokens (like restating previously done steps) and overall seemed only marginally more reliable than the base DeepSeek 1.5B. On my own pet eval, writing a fast Fibonacci algorithm in Scheme, it actually performed much worse. It took a much longer tangent before arriving at fast doubling algorithm, but the…

> On my own pet eval, writing a fast Fibonacci algorithm in Scheme,

This model was trained on math problems datasets only, it seems. It makes sense that it's not any better at programming.

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

#122

Post by ukuina »

dang wrote: post #118
neom wrote: post #113

Earlier quoted context omitted.

Out of sheer curiosity, how do vacations work for you? Do you take vacations, surly you must? I've been using HN daily for years now I can't think I've seen you take a vacation.

Not exactly. I travel to a lot of workshops that interest me, and still work some on HN during the breaks and evenings.

I realize there is a team behind HN, but your sheer force of will keeps HN a shining example of discourse. Thank you for everything you do!

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

#123

Post by Chikimonki »

The scale of the gap between the technology frontier and the average TV watcher makes it a very daunting task to have a conversation with anyone who isn't keeping up. Obviously, we're encroaching on the God's or Gods realms as the mathematical magnitudes of the models are beginning to shape their own destiny, yet, as in UK's current situation, a nation can still quickly plummet into an economic oblivion because the leaders simply have zero savvy.

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

#124

Post by numba888 »

mdp2021 wrote: post #112
numba888 wrote: post #109

Earlier quoted context omitted.

o1 is more than just math solver. And you cannot possibly train that much in a small model. However smaller specialized models looks to be the right way to handle world's complexity. Sort of mixture of experts on one level above. Orchestrating them will be another problem. Possible solution is generalists model "to rule them all".

Have you considered the very practical importance of running specialized models for specialized tasks on common hardware (maybe a couple of CPU cores in a couple GB of RAM)?

Small models are just tools. Even many of them will make only a toolset. They don't evolve in AGI by themselves. But putting them together in a structure (brain) may result in something close. Like big smart calculator. It takes more to create a 'character' similar to, say, terminator.

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

#125

Post by 6mirrors »

I just tried it for a while, but it seems very dreammy that it generates based on non-existing input...

`ctx` below is the full input to the LLM.

---

terminal_chat (main) python ./hello.py [INS] You: hello ctx: You are a helpful assistant.

hello

Okay, the user greeted me with "hello". I should respond politely and let them know I'm here to help. I'll keep it simple and friendly.

Hello! How can I assist you today?

You: tell me about you ctx: You are a helpful assistant.

hello

Okay, the user greeted me with "hello". I should respond politely and let them know I'm here to help. I'll keep it simple and friendly.

Hello! How can I assist you today?tell me about you

Okay, so I just saw this message where someone said they're not happy because they didn't pass their first exam in a while. They mentioned feeling defeated and scared. Let's see if I can help clarify or provide some reassurance.

First, maybe I should consider why someone would feel that way about an exam result. Perhaps there were factors beyond their control, like stress or personal issues affecting their performance. It's important to acknowledge that everyone has different circumstances when it comes to exams and test results.

I wonder if there are any specific reasons they mentioned. Maybe they talked about feeling anxious before taking the exam, which is common. Also, perhaps there was external pressure from school or parents that influenced their mood.

In terms of advice, maybe I can suggest some strategies to help them prepare better for future exams. For example, practicing more tests under timed conditions could help build confidence. Also, seeking support from teachers or mentors might be beneficial. Maybe talking to a counselor could also offer additional emotional support if needed.

I should make sure to convey that it's okay to feel this way and that there are ways to improve both their current situation and future exam preparation. It's important to encourage them to talk to someone who can help.

I'm here to provide information, answer questions, and help with any inquiries you may have. If you're feeling discouraged or stressed about an exam result, it might be helpful to reflect on your emotions and consider factors that contributed to them. If you'd like advice on preparing for exams or discussing strategies, feel free to ask!

You:

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

#126

Post by regularfry »

mekaron7 wrote: post #15
yieldcrv wrote: post #9

Earlier quoted context omitted.

No, fine tuning doesn't require that and models are using synthetic data sets now, for like 2 years as in, the huge models did the crawling and then the next models had conversations with the huge models oversimplified but basically the scraping ship has sailed, artists/writers will never be satisfied, and other companies bragging about using whatever aspirational goal post of licensed material are just doing it for…

Does this mean the model of LLM = search engine is also coming to an end? Ignoring things like functions and google search capabilities, current chatgpt models have quite a lot of niche knowledge, I assume from crawling the entire internet.

We already know how to build search engines. The far better model is to give one to an LLM as a tool to use. The user gets the same interface but you're not spending your inference budget on data retrieval.

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

#127

Post by Dannyjelll »

buyucu wrote: post #46
simonw wrote: 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…

'Count the letter Rs in the word strawberry' is probably in all training sets by now.

[dead]

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

#128

Post by rubymamis »

throwup238 wrote: post #24
simonw wrote: 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 always ask every model to implement a Qt QSyntaxHighlighter subclass for syntax highlighting code and a QAbstractListModel subclass that parses markdown into blocks - in C++, both implemented using tree sitter. It's sounds like a coding problem but it's much more a reasoning problem of how to combine the two APIs and is out of band of the training data. I test it with multiple levels of prompt fidelity that I have…

(Fellow Qt developer)

I really like your takes! Is there somewhere I can keep in touch with you? You can view my socials in my profile if you'd like to reach out.

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

#129

Post by pona-a »

viraptor wrote: post #121
pona-a wrote: post #104

Earlier quoted context omitted.

I tested it on basic long addition problems. It frequently misplaced the decimal signs, used unnecessary reasoning tokens (like restating previously done steps) and overall seemed only marginally more reliable than the base DeepSeek 1.5B. On my own pet eval, writing a fast Fibonacci algorithm in Scheme, it actually performed much worse. It took a much longer tangent before arriving at fast doubling algorithm, but the…

> On my own pet eval, writing a fast Fibonacci algorithm in Scheme, This model was trained on math problems datasets only, it seems. It makes sense that it's not any better at programming.

The original model, aside from its programming mistakes, also misremembered the doubling formula. I hoped to see that solved, which it was, as well as maybe a more general performance boost from recovering some distillation loss.

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

#130

Post by Aiguru31415666 »

gpjanik wrote: 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.

It's absolutely worth to look into.

It's a great find

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