1 citations · 1 across the 5 of their papers we have counts for
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Frustratingly Simple Black-Box Adaptation of Language Models via Logit Bias
Ofek I. Cohen, Lior Shani, Aviv Rosenberg +3
Many organizations aim to adapt language models for internal use, both to improve performance on domain-specific tasks and to address privacy concerns around sensitive data. Howeve…
Quantile of Means: A Bonus-Free Ensemble Method for Minimax Optimal Reinforcement Learning
Asaf Cassel, Aviv Rosenberg
Optimal Reinforcement Learning (RL) algorithms typically rely on carefully constructed count-based uncertainty estimates to drive exploration. Although theoretically sound, such es…
Reinforcement Learning with Discrete Diffusion Policies for Combinatorial Action Spaces
Haitong Ma, Ofir Nabati, Aviv Rosenberg +7
Reinforcement learning (RL) struggles to scale to large, combinatorial action spaces common in many real-world problems. This paper introduces a novel framework for training discre…
Building Math Agents with Multi-Turn Iterative Preference Learning
Wei Xiong, Chengshuai Shi, Jiaming Shen +10
Recent studies have shown that large language models' (LLMs) mathematical problem-solving capabilities can be enhanced by integrating external tools, such as code interpreters, and…
Warm-up Free Policy Optimization: Improved Regret in Linear Markov Decision Processes
Asaf Cassel, Aviv Rosenberg
Policy Optimization (PO) methods are among the most popular Reinforcement Learning (RL) algorithms in practice. Recently, Sherman et al. [2023a] proposed a PO-based algorithm with…
Multi-turn Reinforcement Learning from Preference Human Feedback
Lior Shani, Aviv Rosenberg, Asaf Cassel +10
Reinforcement Learning from Human Feedback (RLHF) has become the standard approach for aligning Large Language Models (LLMs) with human preferences, allowing LLMs to demonstrate re…