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Research questionHow sparse can token-level supervision be in on-policy post-training without weakening language-model reasoning?Post-training reasoning often applies learning signals to every generated token, creating a substantial supervision burden. It remains unclear whether a few critical token-level signals can improve multi-step reasoning as effectively as dense supervision.
LLM Pretraining & Post-training
Machine Learning
Reasoning
Reinforcement Learning
Latest papersRecent research connected to this question, newest first.Extremely Sparse Supervision Incentivizes Reasoning AbilityThe evidence covers on-policy distillation and PPO-based reinforcement learning with verifiable rewards across Qwen3 and Llama configurations, mathematical and coding reasoning, and different model scales. Reported experiments examine supervision as sparse as one or two tokens per reasoning trajectory; broader tasks and deployment settings are not established.research paper · Sep 3, 2026
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