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Matching papers

Agent-Reflex: Teaching Language Agents to Act Reflectively

192 upvotes · 13 OCT 2026 · Guanting Dong, Xinyu Yang, Jiajie Jin et al.

This paper teaches language agents to reflect on their actions and adapt to changing environments, allowing them to learn from feedback and correct mistakes more effectively. Practitioners can benefit from this approach in real-world applications where agents need to handle noisy feedback and user corrections.

continual learninglanguage modelsreflection learningreinforcement learningagent training

FACET: Preserving Source Intent and Executable State in Terminal Task Synthesis

115 upvotes · 19 AUG 2026 · Kou Shi, Zun Wang, Qisheng Su et al.

This paper develops a method to generate high-quality terminal tasks for training agents, ensuring that the tasks accurately reflect the original instruction and environment, and providing a way to validate and improve the generated tasks. Practitioners in AI and robotics may care about this work because it addresses a common challenge in training terminal agents.

continual learningscalable executable supervisionterminal task synthesismulti-stage synthesisagent trainingtask validation