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#self-distillation

2 curated events
papersTODAY 04:00 UTC

arXiv paper critiques on-policy self-distillation, proposes RL contrastive method

A revised arXiv paper examines on-policy self-distillation, a technique that gives reasoning models dense token-level feedback by matching their output distribution to one produced with extra context such as a verified solution. The authors argue this approach yields a flawed distribution, and they introduce RLCSD, which combines reinforcement learning with contrastive self-distillation on policy. The work is a preprint and has not been peer reviewed.

self-distillation/reinforcement-learning/reasoning/arxiv
papersSEP 12 04:00 UTC

arXiv Paper Proposes Solver-Informed Self-Distillation for Operations Research LLMs

A new arXiv preprint introduces a post-training method that uses solver feedback to guide self-distillation, aiming to improve how language models turn natural-language problem descriptions into operations research formulations. The approach is positioned as a way to go beyond training on verified answers alone when bootstrapping such models.

arxiv/self-distillation/operations-research/llm-training