2 citations · 4 across the 11 of their papers we have counts for
5 papers · 1 filter
Agentic evolution of physically constrained foundation models
Jiangwei Zhang, Wen Sun, Chong Wang +7
Artificial intelligence increasingly drives automated scientific discovery, yet contemporary generalist agents lack physical grounding, frequently hallucinating hardware-incompatib…
CLIPer: Tailoring Diverse User Preference via Classifier-Guided Inference-Time Personalization
Jinyan Su, Jinpeng Zhou, Claire Cardie +1
Personalized LLMs can significantly enhance user experiences by tailoring responses to preferences such as helpfulness, conciseness, and humor. However, fine-tuning models to addre…
: Better Prompt Optimization with Fewer Prompts
Zhaolin Gao, Yu, Wang +4
Prompt optimization improves language models without updating their weights by searching for a better system prompt, but its effectiveness varies widely across tasks. We study what…
KARL: Knowledge Agents via Reinforcement Learning
Jonathan D. Chang, Andrew Drozdov, Shubham Toshniwal +23
We present a system for training enterprise search agents via reinforcement learning that achieves state-of-the-art performance across a diverse suite of hard-to-verify agentic sea…
LLMs Can Learn to Reason Via Off-Policy RL
Daniel Ritter, Owen Oertell, Bradley Guo +3
Reinforcement learning (RL) approaches for Large Language Models (LLMs) frequently use on-policy algorithms, such as PPO or GRPO. However, policy lag from distributed training arch…