AI

Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?

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Briefings

The Learn2Play Bench evaluates LLM agents' ability to learn from experience in unfamiliar environments by using novel text-based games. Key findings show that retaining full action records supports better learning than summarizing them, human players outperform agents in peak scores, and agent harnesses significantly improve performance while reducing inference costs. The study compares backbone models, self-evolving methods, and different harness configurations to identify optimal learning strategies.

Key points

• The Learn2Play Bench introduces novel text-based games with counterintuitive rules requiring agents to learn through interaction rather than relying on pretrained knowledge.
• Agents retain complete action and feedback records more effectively than when they summarize experiences into rules or strategies.

Context

This research addresses the limitation of existing benchmarks that evaluate tasks with provided instructions or familiar rules, which cannot distinguish between learned experience and existing knowledge. The benchmark provides reproducible feedback and automatic scoring to enable controlled evaluation of learning across repeated attempts.

Limitations

The study evaluates three specific findings regarding experience retention, human-agent performance gaps, and harness impacts. It does not provide data on long-term adaptation over extended periods, nor does it account for environmental changes beyond the initial game instances.

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