Internalizing Agency from Reflective Experience
Large language models are increasingly deployed as autonomous agents that must plan, act, and recover from mistakes through long-horizon interaction with environments that provide rich feedback. However, prevailing outcome-driven post-training methods (e.g., RL with verifiable rewards) primarily optimize final success signals, leaving rich environment feedback underutilized. Consequently, they often lead to distribution sharpening: the policy becomes better at reproducing a narrow set of already-successful behaviors, while failing to improve the feedback-grounded agency needed to expand problem-solving capacity (e.g., Pass@k) in long-horizon settings. To address this, we propose LEAFE (Le...
| Authors | Rui Ge, Yichao Fu, Yuyang Qian et al. |
|---|---|
| Primary category | cs.AI |
| Published | 17 March 2026 |
| Last updated at source | 17 March 2026 |
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