A new research paper proposes a two-stage approach called OPD-then-RL for improving large language models' reasoning capabilities. This method combines On-Policy Distillation (OPD) with Reinforcement Learning with Verifiable Rewards (RLVR) by first using OPD for broad coverage and then applying RLVR for refinement. Experiments show this sequential strategy outperforms joint optimization methods across logic and math reasoning benchmarks. AI
IMPACT This research suggests a more effective way to combine distillation and reinforcement learning for LLM reasoning, potentially leading to more capable models.
RANK_REASON The cluster contains an academic paper detailing a new method for improving LLM reasoning.
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