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Reinforcement learning with verifiable rewards improves the reasoning capabilities of large language models, but remains computationally expensive and sample inefficient. On-policy distillation (OPD) provides dense token-level supervision from a stronger teacher, yet standard OPD distills every token regardless of whet...
The second KDD Day on AI Reasoning brings together researchers and practitioners from academia and industry to examine how large language models and foundation models can be made more capable, reliable, interpretable, and efficient.
RG-OPD bridges sparse verifier rewards and dense teacher logits, preserving token-level supervision while filtering misleading teacher signals, and produces stronger distilled students, outperforming both vanilla reverse-KL distillation and the recent TSD-KD baseline.
This work proposes AHEAD, a step-aware framework that matches different supervision sources to different step types and solves tasks within tighter interaction budgets than outcome-only RL and prior self-distillation baselines.
Large language models and foundation models are increasingly embedded in reasoning systems that plan, invoke tools, use memory, gather evidence, and iteratively refine their outputs. The second KDD Day on AI Reasoning brings together researchers and practitioners from academia and industry to examine how these systems...
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