4 papers
Interaction-Grounded Learning for Contextual Markov Decision Processes with Personalized Feedback
Mengxiao Zhang, Yuheng Zhang, Haipeng Luo +1
In this paper, we study Interaction-Grounded Learning (IGL) [Xie et al., 2021], a paradigm designed for realistic scenarios where the learner receives indirect feedback generated b…
Flow-DPO: Improving LLM Mathematical Reasoning through Online Multi-Agent Learning
Yihe Deng, Paul Mineiro
Mathematical reasoning is a crucial capability for Large Language Models (LLMs), yet generating detailed and accurate reasoning traces remains a significant challenge. This paper i…
Online Joint Fine-tuning of Multi-Agent Flows
Paul Mineiro
A Flow is a collection of component models ("Agents") which constructs the solution to a complex problem via iterative communication. Flows have emerged as state of the art archite…
Provably Efficient Interactive-Grounded Learning with Personalized Reward
Mengxiao Zhang, Yuheng Zhang, Haipeng Luo +1
Interactive-Grounded Learning (IGL) [Xie et al., 2021] is a powerful framework in which a learner aims at maximizing unobservable rewards through interacting with an environment an…