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Several works have explored multi-agent reinforcement learning (MARL) in LLM collaboration. However, constructing reliable rewards is difficult in practice, as complete and accurate metrics are often unavailable and hard to aggregate. Preference learning provides an alternative by learning from comparative human or AI...
Counteraction-Aware Multi-Teacher On-Policy Distillation is proposed, which addresses issues with decoupled alternating training and gap-based sample selection and outperforms all baselines in general capability recovery while maintaining domain-specific behavior.
ReCo (Reward-Coordinated Compression), a step-wise framework in which a lightweight process-reward estimator scores each completed step and drives three components: reward-adaptive KV-cache compression that shrinks the retained cache harder at high-reward steps and less at low-reward ones, and a confidence-based early...
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