Don't Just Fine-tune the Agent, Tune the Environment

Siyuan Lu (SII), Zechuan Wang, Hongxuan Zhang, Qing Wu, Leilei Gan, chenyi zhuang, Jinjie Gu, Tao Lin

Don't Just Fine-tune the Agent, Tune the Environment: 27 upvotes on Hugging Face Daily Papers, #12 of 57 papers on 2025-10-14. Day-by-day upvote history.

Large Language Model (LLM) agents show great promise for complex, multi-turn tool-use tasks, but their development is often hampered by the extreme scarcity of high-quality training data. Supervised fine-tuning (SFT) on synthetic data leads to overfitting, whereas standard reinforcement learning (RL) struggles with a critical cold-start problem and training instability. To address these challenges, we introduce Environment Tuning, a novel training paradigm that enables agents to learn complex behaviors directly from problem instances without relying on pre-collected expert trajectories. Environment Tuning orchestrates this learning process through a structured curriculum, actionable environment augmentation that provides corrective feedback, and fine-grained progress rewards to ensure stable and efficient exploration. Using only 400 problem instances from Berkeley Function-Calling Leaderboard (BFCL) benchmark, our method not only achieves competitive in-distribution performance against strong baselines but also demonstrates superior out-of-distribution generalization, overcoming the performance collapse common to SFT-based approaches. Our work presents a paradigm shift from supervised fine-tuning on static trajectories to dynamic, environment-based exploration, paving the way for training more robust and data-efficient agents.

Paper page on Hugging Face · arXiv

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