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PaperclipPerfector  5mo ago (text post)   25216 thread views

Culture War Roundup for the week of September 29, 2025

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The first reasoning model for which we have decent documentation, and one which basically defined the research field (as OpenAI/Google/Anthropic hide their recipes) is DeepSeek R1. They've finally had it published in Nature, too. The supplementary has been very informative, because people kept speculating about implementation details.

…we aim to explore the potential of LLMs for developing reasoning abilities through self-evolution in a RL framework, with minimal reliance on human labelling efforts. Specifically, we build on DeepSeek-V3 Base8 and use Group Relative Policy Optimization (GRPO)9 as our RL framework. The reward signal is only based on the correctness of final predictions against ground-truth answers, without imposing constraints on the reasoning process itself. Notably, we bypass the conventional supervised fine-tuning (SFT) phase before RL training. This design choice originates from our hypothesis that human-defined reasoning patterns may limit model exploration, whereas unrestricted RL training can better incentivize the emergence of new reasoning capabilities in LLMs. Through this process, detailed in the next section, our model (referred to as DeepSeek-R1-Zero) naturally developed diverse and sophisticated reasoning behaviours. To solve reasoning problems, the model exhibits a tendency to generate longer responses, incorporating verification, reflection and the exploration of alternative approaches within each response. […] During training, we design a straightforward template to require DeepSeek-R1-Zero to first produce a reasoning process, followed by the final answer. The prompt template is written as below. “A conversation between User and Assistant. The User asks a question and the Assistant solves it. The Assistant first thinks about the reasoning process in the mind and then provides the User with the answer. The reasoning process and answer are enclosed within ... and ... tags, respectively, that is, reasoning process here answer here . User: prompt. Assistant:”, in which the prompt is replaced with the specific reasoning question during training.

Then they do a large number of rollouts for questions, automatically verify, GRPO creates gradients based on answer correctness within each rollout, model is updated, continue for thousands of steps and that's basically it.

As well as the progressive enhancement of reasoning capabilities during training, DeepSeek-R1-Zero also demonstrates self-evolutionary behaviour with RL training. As shown in Fig. 1b, DeepSeek-R1-Zero exhibits a steady increase in thinking time throughout training, driven only by intrinsic adaptation rather than external modifications. Mak- ing use of long CoT, the model progressively refines its reasoning, generating hundreds to thousands of tokens to explore and improve its problem-solving strategies. The increase in thinking time helps with the autonomous develop- ment of sophisticated behaviours. Specifically, DeepSeek-R1-Zero increasingly exhibits advanced reasoning strategies such as reflective reasoning and systematic exploration of alternative solutions provided in Extended Data Fig. 1a, substantially boosting its performance on verifiable tasks such as maths and coding. Notably, during training, DeepSeek-R1-Zero exhibits an ‘aha moment’, shown in Table 1, character- ized by a sudden increase in the use of the word ‘wait’ during reflections, provided in Extended Data Fig. 1b. This moment marks a distinct change in reasoning patterns and clearly shows the self-evolution process of DeepSeek-R1-Zero.

At the time there's been a lot of suspicion about it but now it's pretty solidly settled and replicated science.

Not enough to make a really useful model (I've tested R1-Zero, it was somewhat unhinged), but for reasoning as such. Everything else is basically a quality of life or speed-increasing implementation detail. Using reward models to grade qualitative queries, branching and merging, iterative dataset augmentation and dozens of other tricks add little to the core principle.

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