Unverified50% confidenceFactExact time
In the transfer of LLM reasoning capabilities, knowledge distillation usually refers to training a weak model using chain-of-thought answers generated by a strong model as supervision data, and DeepSeek-R1's distilled series falls into this category.
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Relevance
10/16/2026
First Seen
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The Thinking Leakage Problem in LLM RL Training
hackernewshackernews10/16/2026
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UnverifiedModels such as o1 and DeepSeek-R1 internalize chain-of-thought as a training objective, generating a large amount of internal draft reasoning before producing the final answer.78% similarUnverifiedKnowledge distillation guides the training of a smaller model using soft labels from a larger model, whereas pruning directly inherits knowledge from the weights of the larger model.77% similarUnverifiedIn LLM post-training, the model directly spews intermediate reasoning steps that should have been hidden to the user, or during RL reward computation, the reward model that should only evaluate the final answer sees the reasoning process, thereby distorting the optimization direction.76% similarUnverifiedThe author believes that the 'Chain-of-Thought' output of LLMs is more likely a highly fitted reproduction of reasoning patterns in training data, rather than genuine logical deduction in the true sense.75% similarUnverifiedThe core difference between reasoning models and ordinary LLMs lies in the training approach rather than the architecture; reasoning models introduce a reinforcement learning stage on top of pretraining and supervised fine-tuning.75% similar
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