5 papers · 1 filter
LLM Value Functions for Credit Assignment in Agentic RL
Kaijie Zhu, Weixiang Yan, Omar Attia +3
Reinforcement learning (RL) for large language model (LLM) agents remains challenging in long-horizon tasks with sparse outcome rewards, where assigning credit to individual action…
RLTLDR: Self-improvement by Internalizing Self-generated Feedback
Michael Kirchhof, Eleonora Gualdoni, Andrew Szot +6
The common paradigm of reinforcement learning with verifiable rewards (RLVR) is to let agents make multiple attempts at a task, and optimize towards the successful ones. This becom…
Expanding LLM Agent Boundaries with Strategy-Guided Exploration
Andrew Szot, Michael Kirchhof, Omar Attia +1
Reinforcement learning (RL) has demonstrated notable success in post-training large language models (LLMs) as agents for tasks such as computer use, tool calling, and coding. Howev…
GRACE: A Language Model Framework for Explainable Inverse Reinforcement Learning
Silvia Sapora, Devon Hjelm, Alexander Toshev +2
Inverse Reinforcement Learning aims to recover reward models from expert demonstrations, but traditional methods yield black-box models that are difficult to interpret and debug. I…
From Multimodal LLMs to Generalist Embodied Agents: Methods and Lessons
Andrew Szot, Bogdan Mazoure, Omar Attia +6
We examine the capability of Multimodal Large Language Models (MLLMs) to tackle diverse domains that extend beyond the traditional language and vision tasks these models are typica…