15 citations · 19 across the 9 of their papers we have counts for
6 papers · 1 filter
EasyPPO: Stabilizing the Critic Is Key
Xuanyi Zhou, Qiuyang Mang, Huanzhi Mao +7
A key strength of Proximal Policy Optimization (PPO) is its learned critic, which uses historical trajectories collected during reinforcement learning to estimate expected returns…
Fantastic Adaptive Taxonomies and How to Use Them
Mert Cemri, Andrei Cojocaru, Melissa Pan +9
An agent system's execution traces record how it fails, and procedures that improve such a system without changing model weights (trajectory selection, prompt and workflow optimiza…
Continual Learning Bench: Evaluating Frontier AI Systems in Real-World Stateful Environments
Parth Asawa, Christopher M. Glaze, Gabriel Orlanski +7
Continual learning, the ability of AI systems to improve through sequential experience, has attracted substantial interest, but no high-quality benchmark exists to evaluate it. We…
Concurrency without Model Changes: Future-based Asynchronous Function Calling for LLMs
Guangyu Feng, Huanzhi Mao, Prabal Dutta +1
Function calling, also known as tool use, is a core capability of modern LLM agents but is typically constrained by synchronous execution semantics. Under these semantics, LLM deco…
FrontierSmith: Synthesizing Open-Ended Coding Problems at Scale
Runyuan He, Qiuyang Mang, Shang Zhou +14
Many real-world coding challenges are open-ended and admit no known optimal solution. Yet, recent progress in LLM coding has focused on well-defined tasks such as feature implement…
Learning, Fast and Slow: Towards LLMs That Adapt Continually
Rishabh Tiwari, Kusha Sareen, Lakshya A Agrawal +6
Large language models (LLMs) are trained for downstream tasks by updating their parameters (e.g., via RL). However, updating model parameters forces them to absorb task-specific in…