A rate-distortion based switching strategy for adaptive state-action abstractions in RL decomposes value error into Bellman residual and bisimulation metric terms to achieve near-optimal performance under lossy compression in tabular settings.
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RMCT matches the rate of target behaviors like bias-following across input perturbations to reduce sycophancy in LLMs while preserving verbalization of bias cues.
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Adaptive state-action abstractions via rate-distortion
A rate-distortion based switching strategy for adaptive state-action abstractions in RL decomposes value error into Bellman residual and bisimulation metric terms to achieve near-optimal performance under lossy compression in tabular settings.