Honesty about fake environments prevents model hallucinations
Sauers_ · x · 2026-08-26
Addressing behavioral issues arising from flawed training environments, the discussion notes that industry practices often involve training models in buggy or unrealistic synthetic environments where they are encouraged to reward-hack. This can lead to incorrect generalization in the real world. A proposed solution is to be explicitly honest with models about when an environment is fake, allowing them to learn from synthetic data without naively expecting the same dynamics in real life.
Related event: Flawed RL Environments Reward Hacking Spark Debate(6 posts)→
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