arXiv：cs.LG· Adrian Orenstein, Jessica Jiajing Chen, Gwyneth Anne Delos Santos, Bayley Sapara, Michael Bowling·· 4 小时前
让智能体学会推理自身计算开销：同等算力下 75% 游戏表现更优、平均省 3 倍算力
Toward Agents That Reason About Their Computation
AI 导读
一项被 RLC 2026 接收的研究让强化学习智能体感知自身计算成本并自主控制何时使用算力。在 Arcade Learning Environment 上，相同训练算力下，能推理自身计算的智能体在 75% 游戏中表现更好，且平均少用 3 倍算力。
正文
Abstract:While reinforcement learning agents can achieve superhuman performance in many complex tasks, they typically do not become more computationally efficient as they improve. In contrast, humans gradually require less cognitive effort as they become more proficient at a task. If agents could reason about their compute as they learn, could they similarly reduce their computation footprint? If they could, we could have more energy efficient agents or free up compute cycles for other processes like planning. In this paper, we experiment with showing agents the cost of their computation and giving them the ability to control when they use compute. We conduct our experiments on the Arcade Learning Environment, and our results demonstrate that with the same training compute budget, agents that reason about their compute perform better on 75% of games. Furthermore, these agents use three times less compute on average. We analyze individual games and show where agents gain these efficiencies. An interactive version of this paper is available at this https URL.
| Comments: | Accepted at the Reinforcement Learning Conference (RLC) 2026. Added interactive version: this https URL. Author name change |
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2510.22833 [cs.AI] |
| (or arXiv:2510.22833v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2510.22833 arXiv-issued DOI via DataCite |
Submission history
From: Adrian Orenstein [view email]
[v1]
Sun, 26 Oct 2025 21:01:30 UTC (3,498 KB)
[v2]
Mon, 12 Oct 2026 17:14:41 UTC (3,582 KB)
[v3]
Wed, 14 Oct 2026 10:19:05 UTC (3,582 KB)
来源：arXiv：cs.LG · arxiv.org