| title | EnvLunch: The Environment as a Free Lunch for Learning to Interact |
|---|---|
| authors | Wei Liu, Min Gao, Yali Du, Yulan He |
| date | 2026-10-24 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.33817 |
| affiliation | King's College London |
| upvotes | 2 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | For agentic data analysis, language-model agents often require a large number of annotated tasks to learn how to interact effectively with executable environments (i.e., databases). While high-quality annotated tasks require heavy manual effort, an executable environment already provides rich interaction structure and verifiable outcomes. In this sense, the environment itself offers a free lunch, an additional set of verifiable tasks that require no human annotation. We propose EnvLunch, a warm-up stage that turns this environmental signal into agent training without human-written or model-generated task descriptions, before downstream agentic reinforcement learning. EnvLunch samples valid action trajectories, executes them to obtain final observations, and trains an agent to recover a functionally equivalent trajectory given only its final observation. This label-free warm-up enables the model to outperform the baseline by over 9 points on average across all downstream training budgets, and with few labels, the baseline needs 8 to 32 times as many annotated examples to match the accuracy that the EnvLunch model reaches at its smallest training budget. Letting the environment alone define the task yields a larger gain than having the model write its own tasks over the same environments, and this warm-up also improves fine-tuning on benchmarks beyond data analysis. EnvLunch therefore saves annotated tasks and provides a better starting point for fine-tuning agentic models when such tasks are limited. |
cite
@misc{liu2026envlunch,
title={EnvLunch: The Environment as a Free Lunch for Learning to Interact},
author={Wei Liu and Min Gao and Yali Du and Yulan He},
year={2026},
eprint={2610.33817},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.33817}
}