AI

Feedback That Backfires in Games: Do LLM Players Repeat the Move They Just Watched Fail?

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 23/10/2026, 04:03:24 UTC.

Briefings

Research on LLM agents in Sokoban, Connect Four, and Othello reveals that recording failed moves increases the likelihood of repeating them, creating a negative feedback loop. This effect is driven by the move's surface form and occurs across instruction-tuned models ranging from 135M to 1.7B parameters. The phenomenon is absent for losing moves and varies significantly by model architecture and training stage, with OLMo 2 1B models showing the inversion in their base versions before RL training.

Key points

• LLM agents in Sokoban, Connect Four, and Othello record failed moves, which increases the probability of repeating the failed action by 1.38 nats per token.
• The effect persists across instruction-tuned models from 135M to 1.7B parameters, though it is absent for losing moves and varies by model architecture.

Context

This study investigates whether LLM agents repeat failed moves in games, a behavior that could hinder learning. The findings are based on experiments in Sokoban, Connect Four, and Othello using fixed probe items and perturbed correct moves to verify failure conditions.

Limitations

Measurements were conducted on a free Colab GPU in float32, limiting generalizability. The study relies on a single preprint (arXiv:2610.30205v1) and does not include additional background context or further validation.

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