ProAct: Agentic Lookahead in Interactive Environments

• URL: http://arxiv.org/abs/2602.05327v1
• Date: Thu, 05 Feb 2026 05:45:16 GMT
• Title: ProAct: Agentic Lookahead in Interactive Environments
• Authors: Yangbin Yu, Mingyu Yang, Junyou Li, Yiming Gao, Feiyu Liu, Yijun Yang, Zichuan Lin, Jiafei Lyu, Yicheng Liu, Zhicong Lu, Deheng Ye, Jie Jiang,
• Abstract summary: ProAct is a framework that enables agents to internalize accurate lookahead reasoning through a two-stage training paradigm.<n>We introduce Grounded LookAhead Distillation (GLAD), where the agent undergoes supervised fine-tuning on trajectories derived from environment-based search.<n>We also propose the Monte-Carlo Critic (MC-Critic), a plug-and-play auxiliary value estimator designed to enhance policy-gradient algorithms.
• Score: 56.50613398808361
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing Large Language Model (LLM) agents struggle in interactive environments requiring long-horizon planning, primarily due to compounding errors when simulating future states. To address this, we propose ProAct, a framework that enables agents to internalize accurate lookahead reasoning through a two-stage training paradigm. First, we introduce Grounded LookAhead Distillation (GLAD), where the agent undergoes supervised fine-tuning on trajectories derived from environment-based search. By compressing complex search trees into concise, causal reasoning chains, the agent learns the logic of foresight without the computational overhead of inference-time search. Second, to further refine decision accuracy, we propose the Monte-Carlo Critic (MC-Critic), a plug-and-play auxiliary value estimator designed to enhance policy-gradient algorithms like PPO and GRPO. By leveraging lightweight environment rollouts to calibrate value estimates, MC-Critic provides a low-variance signal that facilitates stable policy optimization without relying on expensive model-based value approximation. Experiments on both stochastic (e.g., 2048) and deterministic (e.g., Sokoban) environments demonstrate that ProAct significantly improves planning accuracy. Notably, a 4B parameter model trained with ProAct outperforms all open-source baselines and rivals state-of-the-art closed-source models, while demonstrating robust generalization to unseen environments. The codes and models are available at https://github.com/GreatX3/ProAct

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