3 papers
cs.LG2026
Co-Evolution of Policy and Internal Reward for Language Agents
Xinyu Wang, Hanwei Wu, Jingwei Song +8
Large language model (LLM) agents learn by interacting with environments, but long-horizon training remains fundamentally bottlenecked by sparse and delayed rewards. Existing metho…
cs.LG2025
LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet?
Rushil Gupta, Jason Hartford, Bang Liu
Large language models (LLMs) have recently been proposed as general-purpose agents for experimental design, with claims that they can perform in-context experimental design. We eva…
cs.AI2024
Pairing Analogy-Augmented Generation with Procedural Memory for Procedural Q&A
K Roth, Rushil Gupta, Simon Halle +1
Large language models struggle to synthesize disparate pieces of information into a coherent plan when approaching a complex procedural task. In this work, we introduce a novel for…