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Re:An analysis of DeepSeek's R1-Zero and R1 by sheepscreek on Wednesday January 29, 2025 @05:14PM (#42871926)
> The most promising idea is to use reasoning models to generate data, and then train our non-reasoning models with the reasoning-embedded data.

DeepSeek did precisely this with their LLama fine-tunes. You can try the 70B one here (might have to sign up): https://groq.com/groqcloud-makes-deepseek-r1-distill-llama-7...

Re:An analysis of DeepSeek's R1-Zero and R1 by spyckie2 on Wednesday January 29, 2025 @08:49PM (#42873838)
Yes, but I meant it slightly differently than the distills.

The idea is to create the next gen SOTA non reasoning model with synthetic reasoning training data.

Re:An analysis of DeepSeek's R1-Zero and R1 by entropicdrifter on Thursday January 30, 2025 @12:23PM (#42879898)
So you mean something like, "what if the baseline, off-the-cuff response for the next-gen models was tuned based on the results of the reasoning model excluding the reasoning itself?"
Re:An analysis of DeepSeek's R1-Zero and R1 by spyckie2 on Thursday January 30, 2025 @04:17PM (#42882278)
Exactly, albeit it may need the reasoning later to form the proper foundational logic in the weights.