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Research questionHow can on-policy reasoning models use dense self-guidance without reinforcing incorrect solutions?Terminal verifiers give dependable outcome signals but only at the end of a trajectory, whereas dense same-model guidance can amplify false confidence. This tension can cause training to favor incorrect responses, collapse response lengths, or concentrate learning on a narrow set of reasoning strategies.
AI
LLM Pretraining & Post-training
Machine Learning
Reasoning
Reinforcement Learning
Latest papersRecent research connected to this question, newest first.FlowBalance: Verifier-Grounded Self-Improvement from On-Policy Reasoning ExperienceThe source studies mathematical reasoning with on-policy language-model trajectories, terminal outcome verification, and token-level guidance from a frozen training-time view of the same policy. Reported evidence covers Qwen3-4B and Qwen3-8B experiments, including an AIME24 strategy-diversity diagnostic; broader generalization is not established by the supplied evidence.research paper · Sep 3, 2026
Related questions
Should reasoning LLMs combine on-policy distillation and verifiable-reward RL jointly or sequentially during post-training?How can reasoning models keep improving on open-ended agentic tasks as human supervision and reliable rewards recede?How sparse can token-level supervision be in on-policy post-training without weakening language-model reasoning?How can terminal-agent training environments stay challenging as models improve without costly on-policy synthesis?