SENTINEL: Failure-Driven Reinforcement Learning for Training Tool-Using Language Model Agents

arXiv:2606.12908v1 Announce Type: new Abstract: Language model agents are increasingly effective in solving realistic tasks through multi-turn tool use. However, training reliable tool-using agents remains challenging in practice. While reinforcement learning provides an on-policy paradigm for improving agents from their own environment interactions, its effectiveness depends heavily on the training task distribution. When tasks are fixed before training, the task distribution can become increasingly mismatched with the policy's evolving capabilities, causing many rollouts to be spent on uninf

Source: arXiv cs.CL — read the full report at the original publisher.

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