1 citations · 2 across the 5 of their papers we have counts for
4 papers · 1 filter
Generative Modeling of Discrete Latent Structures via Dynamic Policy Gradients
Stefan Ivanovic, Ge Liu, Mohammed El-Kebir
Many scientific problems require inferring unobserved mechanistic latent states from indirect observations. While classical approaches, including expectation maximization, do not s…
From Supervision to Exploration: What Does Protein Language Model Learn During Reinforcement Learning?
Hanqun Cao, Hongrui Zhang, Junde Xu +12
Protein language models (PLMs) have advanced computational protein science through large-scale pretraining and scalable architectures. In parallel, reinforcement learning (RL) has…
Position: The Hidden Costs and Measurement Gaps of Reinforcement Learning with Verifiable Rewards
Fang Wu, Aaron Tu, Weihao Xuan +21
Reinforcement learning with verifiable rewards (RLVR) is a practical, scalable way to improve large language models on math, code, and other structured tasks. However, we argue tha…
Lightweight MSA Design Advances Protein Folding From Evolutionary Embeddings
Hanqun Cao, Xinyi Zhou, Zijun Gao +7
Protein structure prediction often hinges on multiple sequence alignments (MSAs), which underperform on low-homology and orphan proteins. We introduce PLAME, a lightweight MSA desi…