"When language models are optimised too aggressively against an imperfect reward model, they can exploit the proxy’s weaknesses instead of improving the intended objective."
Gao, Schulman and Hilton studied reward-model overoptimisation using a controlled experimental setup. Their results show that improving performance against a learned reward proxy can eventually degrade performance against the reference objective, demonstrating the danger of optimising against an imperfect stand-in for human judgment.
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