163 citations · 216 across the 21 of their papers we have counts for
4 papers · 1 filter
ECHO: Terminal Agents Learn World Models for Free
Vaishnavi Shrivastava, Piero Kauffmann, Ahmed Awadallah +1
CLI agents are the closest thing language models have to an embodied setting: the model emits commands, the terminal executes them, and the returned stream -- stdout, errors, files…
Scaling Agentic Capabilities, Not Context: Efficient Reinforcement Finetuning for Large Toolspaces
Karan Gupta, Pranav Vajreshwari, Yash Pandya +3
Agentic systems operating over large tool ecosystems must plan and execute long-horizon workflows under weak or non-verifiable supervision. While frontier models mitigate these cha…
Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF
Tengyang Xie, Dylan J. Foster, Akshay Krishnamurthy +3
Reinforcement learning from human feedback (RLHF) has emerged as a central tool for language model alignment. We consider online exploration in RLHF, which exploits interactive acc…
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences
Corby Rosset, Ching-An Cheng, Arindam Mitra +3
This paper studies post-training large language models (LLMs) using preference feedback from a powerful oracle to help a model iteratively improve over itself. The typical approach…