EPO: Entropy-regularized Policy Optimization for LLM Agents
  Reinforcement Learning
Authors: name: Xu Wujiang, name: Wentian Zhao, name: Zhenting Wang, name: Li Yu-Jhe, name: Jin Can, name: Jin Mingyu, name: Mei Kai, name: Wan Kun, name: Metaxas Dimitris

Training LLM agents in multi-turn environments with sparse rewards, where
completing a single task requires 30+ turns of interaction within an episode,
presents a fundamental challenge for reinforcement learning. We identify a
critical failure mode unique to this setting: the exploration-exploitation
cascade failure. This cascade begins with early-stage policy premature
convergence, where sparse feedback causes agents to commit to flawed,
low-entropy strategies. Subsequently, agents enter late-stage policy collapse,
where conventional entropy regularization becomes counterproductive, promoting
chaotic exploration that destabilizes training. We propose Entropy-regularized
Policy Optimization (EPO), a general framework that breaks this failure cycle
through three synergistic mechanisms: (1) adopting entropy regularization in
multi-turn settings to enhance exploration, (2) an entropy smoothing
regularizer that bounds policy entropy within historical averages to prevent
abrupt fluctuations, and (3) adaptive phase-based weighting that balances
exploration and exploitation across training. Our analysis justifies that EPO
guarantees monotonically decreasing entropy variance while maintaining
convergence. EPO achieves up to 152% performance improvement on ScienceWorld
and up to 19.8% on ALFWorld. Our work demonstrates that multi-turn
sparse-reward settings require fundamentally different entropy control than
traditional RL, with broad implications for LLM agent training.