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Reward hacking, where a model exploits an evaluation signal without completing the intended task, threatens the reliability of deployed language model systems. Existing datasets use different labels, response formats, and metadata conventions, making detector results difficult to compare. We present RH-Detect, a benchm...
Offline goal-conditioned reinforcement learning (GCRL) has produced a diverse set of goal-reaching algorithms, yet no single algorithm performs best across environments, goals, and even different phases of the same task. Rather than deploying only the best-performing policy, we ask whether a set of frozen goal-conditio...
This work analyzes the optimal design of the subtokenizer that minimizes MDM-Prime training objective and develops MDM-Prime-v2, a masked diffusion language model which incorporates Binary Encoding and Index Shuffling.
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