VRL-Bench: Benchmarking agents on computer control tasks under finite trial budgets
Learning from trial and error is a promising way to improve language agents on complex tasks such as computer control. Reflexion introduced verbal reinforcement learning, which turns failed trials into text that guides later attempts without updating model parameters. We introduce VRL-Bench, a harness for fair evaluation of trial-and-error learning under finite trial budgets. Across three models on MiniWoB and WebShop, we evaluate updates from several prominent verbal-memory methods spanning Reflexion and later work: each improves observed success over memory-free retry in some settings but reduces it in others. Replay experiments show that using reflection can reduce success rates, revea...
| Authors | Yu Bai, Yukai Miao, Dawei Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 11 September 2026 |
| Last updated at source | 11 September 2026 |
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