Unverified50% confidenceOpinionExact time
The community speculates that large-scale reasoning chain distillation partly explains the success of certain models' RL training, but this is speculation rather than a definitive causal conclusion from the paper.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
9/22/2026
First Seen
Valid until: 12/21/2026
Sources
Related Claims
UnverifiedThe core assumption of the research is that the residual stream representations inside the model have already implicitly encoded information about whether a task will succeed.72% similarUnverifiedAfter reasoning models such as DeepSeek-R1 and Qwen3 pushed chain-of-thought training to its extreme, side effects emerged including the model repeatedly self-verifying, frequently backtracking, and generating large amounts of inner monologue on simple questions.70% similarUnverifiedThe study does not deny the value of reasoning-type models, holding that their performance improvements are real; what it denies is the way in which those improvements are explained.70% similarUnverified思维链推理的效果随模型规模呈现涌现性，在较小模型上几乎无效，在足够大的模型上才能显著提升推理准确率70% similarUnverifiedMLA integrates low-rank structure into model weights during the training phase, whereas the community experiment applies post-processing approximation to existing models during the inference phase.69% similar
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