38 citations · 66 across the 37 of their papers we have counts for
12 papers · 1 filter
MESH-Harness: Self-Improving Agent Harnesses via Bandit-Guided Compositional Evolution
Zhiwei Shang, Yu Huo, Mingrong Gong +6
An agent harness is the code that organizes context, maintains state, and coordinates tool calls for a language model. We study how to improve the harness under a limited evaluatio…
RISED: RubrIcs for agentic multi-environment Selection and sElf-Distillation
Jingtan Wang, Sirajul Salekin, Young mok Jung +3
Training a single LLM agent jointly across diverse interactive environments has attracted increasing attention as a route to generalist agents. Existing curriculum and data-selecti…
COBRA-Skills: Contextual Bandit-Guided Evolution for Agent Skill Optimization
Pingchen Lu, Xiangyi Wang, Xiang Li +6
Large language model (LLM) agents can benefit from reusable skills distilled from prior task experience, yet existing skill optimization methods often rely on costly execution-base…
Efficiently Estimating Optimal Hyperparameter Scaling Laws through Power-Law Entropy Search
Zhiliang Chen, Sebastian Ament, David Eriksson +4
Optimal hyperparameter scaling laws describe how the best hyperparameters for large language model (LLM) training change with model and data scale, enabling practitioners to predic…
Scaling Near-Optimal SFT-RL Annotation Budget Allocation from Small to Large LLMs
Jingtan Wang, Arun Verma, Xiaoqiang Lin +4
How to divide a fixed annotation budget between supervised fine-tuning (SFT) and reinforcement learning (RL) during LLM post-training remains an open problem. Existing work charact…
Nonlinear Axiomatic Attribution for Cooperative Games
Weida Li, Zhuanghua Liu, Yaoliang Yu +1
The Shapley value is a widely used concept in attribution problems, as it uniquely satisfies the axioms of linearity, consistency, equal treatment, and efficiency. Often, the inclu…