4 citations · 4 across the 7 of their papers we have counts for
4 papers · 1 filter
Reward-rate Policy Gradient for Efficient Machine Learning Engineering Agents
Muhang Tian, Sherry Yang
Traditional reinforcement learning (RL) techniques focus on maximizing expected cumulative reward, where each action assumes to take a constant unit of time. However, this assumpti…
CrystalReasoner: Reasoning and RL for Property-Conditioned Crystal Structure Generation
Yuyang Wu, Stefano Falletta, Delia McGrath +1
Generative modeling has emerged as a promising approach for crystal structure discovery. However, existing LLM-based generative models struggle with low-level atomic precision, whi…
GIANTS: Generative Insight Anticipation from Scientific Literature
Joy He-Yueya, Anikait Singh, Ge Gao +5
Scientific breakthroughs often emerge from synthesizing prior ideas into novel contributions. While language models (LMs) show promise in scientific discovery, their ability to per…
Learning Next Action Predictors from Human-Computer Interaction
Omar Shaikh, Valentin Teutschbein, Kanishk Gandhi +8
Truly proactive AI systems must anticipate what we will do next. This foresight demands far richer information than the sparse signals we type into our prompts -- it demands reason…