Diagnostics reveal that RL on PLMs is governed by two reward properties: verifiability, whether the reward is a fixed environment or a learned surrogate vulnerable to distribution shift, and coverage, the fraction of sequence space giving an informative gradient.
Hanqun Cao, Hongrui Zhang, Junde Xu et al.· Proceedings of the 32nd ACM...· 0 citations
EMES is the first work to incorporate EM into controllable DMs and to leverage EM for guiding data synthesis in RL, and introduces a hashing-based state representation for EM to improve its efficiency and further boost the quality of synthetic data.
Reinforcement learning (RL) is increasingly applied to Protein Language Models (PLMs), yet its effectiveness varies across tasks, and standard metrics such as pass@k can rise even when the model's solvable problem set is shrinking. We introduce two capability-level diagnostics. The Expansion-Shrinkage Ratio (ESR) measu...
Hanqun Cao, Hongrui Zhang, Junde Xu et al.· Proceedings of the 32nd ACM...· 0 citations
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