Researchers have introduced RISE, a novel method for improving language model post-training through self-extrapolating policy distillation. This technique constructs a synthetic teacher from the model's own reinforcement learning trajectory, converting sparse outcome-induced updates into dense token-level targets without external models. RISE combines reinforcement learning from human feedback (RLHF) and on-policy distillation (OPD) in a loop, where outcome rewards guide extrapolation and the extrapolated teacher refines token decisions. Experiments show RISE outperforms existing methods across various tasks, including mathematical reasoning, STEM, and code generation. AI
IMPACT This method could lead to more efficient and effective language model training by improving self-distillation techniques.
RANK_REASON The cluster describes a new research paper detailing a novel method for language model training.
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- arXiv
- code generation
- mathematical reasoning
- multi-domain STEM
- multi-turn agentic tasks
- On-Policy Distillation
- policy distillation
- Recursive Improvement via Self-Extrapolating Policy Distillation
- RISE
- RLVR
- Hugging Face
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