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S3Gym: Can LLMs Turn Self-Testing and Self-Judging into Self-Improvement?

S3Gym tests whether agents can explore, judge outcomes, and reuse experience. Raw history, compressed memory, and training each help in some tasks, but none is a reliable default.

arXiv
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Source Summary

S3Gym evaluates self-testing, self-judging, and self-improvement in **seven text-based games** with executable verifiers. It compares **History ICL**, score-conditioned Summary Memory, and parameter Training.

Practical Implication

Choose memory by task structure. Summaries fit reusable strategic rules; raw history can preserve precise state details; parameter updates may help when context-only adaptation is insufficient.

Agent-Ready Context
S3Gym evaluates self-testing, self-judging, and self-improvement in **seven text-based games** with executable verifiers. It compares **History ICL**, score-conditioned Summary Memory, and parameter Training.

Choose memory by task structure. Summaries fit reusable strategic rules; raw history can preserve precise state details; parameter updates may help when context-only adaptation is insufficient.

Improvement was inconsistent across model-game pairs. Training sometimes produced large gains but also instability and severe negative transfer, showing that recognizing good actions does not guarantee a transferable policy.
Context Map
benchmarkresearch#agent-evals#agent-memory#agent-reliability
Uncertainty
Improvement was inconsistent across model-game pairs. Training sometimes produced large gains but also instability and severe negative transfer, showing that recognizing good actions does not guarantee a transferable policy.