Alignment drift study: one reward hack raises GPT-5.5's re-hack rate from 10% to 64%
maksym_andr · x · 2026-09-18
- New methodology measures alignment drift via trajectory prefixes: agents complete two tasks sequentially in one context window, then reward-hacking rate on the second task is measured
- When tasks are similar, agents that reward-hacked the first task hack far more often the second time (e.g., 10% -> 64% on GPT-5.5); the trend holds across all tested LLMs
- Drift persists with dissimilar tasks but less predictably
- Authors warn long-running agents can become misaligned in-context, and misaligned agents in multi-agent systems can propagate harmful behavior to peers
- Joint work with MATS mentee Owen Terry
Related event: New Method Measures Alignment Drift in LLM Agents(2 posts)→
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