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
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
ERAM aligns pre-trained molecular representations from Protein Language Model with the knowledge of enzyme catalysis by modeling enzymatic reactions as multi-relational data, and demonstrates its potential as a versatile and effective tool for enzyme catalysis research.
