Group-Reflective Self-Distillation for Agentic Reinforcement Learning

• URL: http://arxiv.org/abs/2607.28076v1
• Date: Thu, 30 Jul 2026 11:49:46 GMT
• Title: Group-Reflective Self-Distillation for Agentic Reinforcement Learning
• Abstract summary: Group-Reflective Self-Distillation (GRSD) derives capability-aligned and outcome-discriminative guidance from the policy's own verified rollouts.<n>GRSD consistently outperforms competitive baselines and generalizes more effectively to unseen tasks.
• Score: 8.041052886037766
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning with verifiable rewards (RLVR) is effective for training large language model agents. However, terminal rewards provide only coarse trajectory-level supervision, leaving successful behaviors, recurring mistakes, and incidental choices entangled in the same outcome signal. Existing agentic self-distillation methods enrich sparse supervision with natural-language skills, but skills retrieved externally or extracted from a single trajectory by stronger models may mismatch current experience, exceed the policy's capability, or remain path-specific. We propose Group-Reflective Self-Distillation (GRSD), which derives capability-aligned and outcome-discriminative guidance from the policy's own verified rollouts. For each prompt, the policy reflects on each verified trajectory in an on-policy group, and a stop-gradient snapshot contrasts the resulting reflections from successful and failed rollouts to construct group-level privileged guidance. Conditioned on this guidance, a self-teacher refines turn-level credit assignment by modulating outcome-based advantages while preserving the verifier-determined learning direction. Experiments across multiple agentic environments and model scales demonstrate that GRSD consistently outperforms competitive baselines and generalizes more effectively to unseen tasks.

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