4 citations · 4 across the 7 of their papers we have counts for
9 papers
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…
MLE-Smith: Scaling MLE Tasks with Automated Multi-Agent Pipeline
Rushi Qiang, Yuchen Zhuang, Anikait Singh +4
While Language Models (LMs) have made significant progress in automating machine learning engineering (MLE), the acquisition of high-quality MLE training data is significantly cons…
Reinforcement Learning for Machine Learning Engineering Agents
Sherry Yang, Joy He-Yueya, Percy Liang
Existing agents for solving tasks such as ML engineering rely on prompting powerful language models. As a result, these agents do not improve with more experience. In this paper, w…
WorldGym: World Model as An Environment for Policy Evaluation
Julian Quevedo, Ansh Kumar Sharma, Yixiang Sun +3
Evaluating robot control policies is difficult: real-world testing is costly, and handcrafted simulators require manual effort to improve in realism and generality. We propose a wo…
MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering
Rushi Qiang, Yuchen Zhuang, Yinghao Li +8
We introduce MLE-Dojo, a Gym-style framework for systematically reinforcement learning, evaluating, and improving autonomous large language model (LLM) agents in iterative machine…
CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning
Siddharth Verma, Justin Fu, Mengjiao Yang +1
Conventionally, generation of natural language for dialogue agents may be viewed as a statistical learning problem: determine the patterns in human-provided data and generate appro…