RISE method enhances language models via self-extrapolation and policy distillation

Researchers have introduced RISE, a novel method for improving language models through self-extrapolation and policy distillation. Unlike previous approaches that relied on external teachers or limited in-context learning, RISE constructs a synthetic teacher from the model's own training trajectory. This synthetic teacher provides dense, token-level supervision by extrapolating the model's progress. Experiments show RISE outperforms standard reinforcement learning from human feedback (RLHF) and on-policy self-distillation across various tasks, including mathematical reasoning, STEM, and code generation. AI

IMPACT This new self-extrapolation technique could lead to more efficient and effective language model training, potentially improving performance on complex reasoning and generation tasks.

RANK_REASON The cluster contains a research paper detailing a new method for improving language models. [lever_c_demoted from research: ic=1 ai=1.0]

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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

• paper
• model release

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