Wednesday, 14 October 2026 · 5 min read
Agent-Reflex trains language agents to reflect and recover from noisy tool feedback
Agent-Reflex trains language agents to proactively reflect and recover from failures by mining reflection skills from noisy interactions, fine-tuning on supervised reflection data, and applying on-policy reinforcement learning with reflection-guided distillation, improving robustness on tool-use benchmarks.
Based on the paper Agent-Reflex: Teaching Language Agents to Act Reflectively by Guanting Dong, Xinyu Yang, Jiajie Jin, Yuyang Hu and 8 others
Challenges in training robust language agents under noisy real-world conditions
Large language models (LLMs) have evolved into interactive agents capable of long-horizon reasoning and tool use, but real-world deployments expose them to noisy tool feedback and user corrections that existing training environments do not simulate well.
Current agent training often assumes stable, noise-free environments and minimal user intervention, leaving agents unprepared for failure detection, recovery, and user-guided correction in practice.
Reflection learning—agents' ability to recognize and recover from errors—is limited by lack of supervision from realistic noisy interactions and diverse user corrections, creating a gap between idealized training and real deployment.
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