10 papers
ICR-RL: Deep Reinforcement Learning via In-Context Regression
David Schiff, Ofir Lindenbaum, Yonathan Efroni
Recent advancements in machine learning have largely been driven by foundation models (FMs) trained on large, diverse datasets, enabling them to generalize effectively to new, rela…
Neural Parameter Calibration for Finite-State Mean Field Games
Anna C. M. Thöni, Grégoire Lambrecht, Gökçe Dayanıklı +3
Mean field games efficiently approximate a very large population of strategic agents. While these games can aid the understanding of complex systems, their deployment in real-world…
Structure Enables Effective Self-Localization of Errors in LLMs
Ankur Samanta, Akshayaa Magesh, Ayush Jain +8
Self-correction in language models remains elusive. In this work, we explore whether language models can explicitly localize errors in incorrect reasoning, as a path toward buildin…
Credit Assignment with Resets in Language Model Reasoning
Ankur Samanta, Akshayaa Magesh, Ayush Jain +7
Contemporary reinforcement learning with verifiable reward methods post-train language models on multi-step reasoning by assigning a single outcome reward uniformly across all toke…
Hack-Verifiable Environments: Towards Evaluating Reward Hacking at Scale
Amit Roth, Ankur Samanta, Matan Halevy +2
Aligning autonomous agents with human intent remains a central challenge in modern AI. A key manifestation of this challenge is reward hacking, whereby agents appear successful und…
Self-Improvement of Language Models by Post-Training on Multi-Agent Debate
Ankur Samanta, Akshayaa Magesh, Runzhe Wu +7
Self-improvement, where models improve beyond their current performance without external supervision, remains a challenge. The core difficulty is sourcing a training signal stronge…