Agent-Reflex: Teaching Language Agents to Act Reflectively

Large language models have evolved from single-turn chatbots into interactive agents capable of long-horizon reasoning. In real deployments, MCP tool services can return noisy feedback and users may intervene to correct the agent, but existing methods provide limited supervision for reflection learning and failure recovery. To bridge this gap, we propose Agent-Reflex, a general training framework for incentivizing proactive reflection in agents. Specifically, Agent-Reflex comprises three stages: Environment Reflection Skill Discovery (RSD) injects grounded tool-feedback perturbations into environment scaling, samples a capability-diverse LLM pool, mines matched success–failure pairs, and summarizes reusable reflection meta-skills. Building on this skill library, we propose a two-stage reflection training framework: (1) Reflective Supervised Fine-Tuning (ReSFT) converts each skill into supervision for self-reflection, skill recovery, and user-centric correction, strengthening complementary reflective behaviors; and (2) On-Policy Reflective Policy Optimization (ORPO) combines agentic reinforcement learning under noisy interactions with reflection-guided on-policy distillation to further elicit proactive reflection. Experiments on six challenging agent benchmark suites demonstrate the effectiveness of Agent-Reflex. Further analyses show that Agent-Reflex improves agents’ robustness to noise while maintaining stable training dynamics, supporting scalable agent training.