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[2506.06964] Offline RL by Reward-Weighted Fine-Tuning for Conversation Optimization
Summary
This article presents a novel approach to offline reinforcement learning (RL) using reward-weighted fine-tuning, enhancing conversation optimization in large language models (LLMs).
Why It Matters
The research addresses the limitations of existing offline RL methods by proposing a more efficient framework that directly optimizes for rewards without the complexity of additional hyper-parameters. This advancement is significant for improving the performance of conversational agents, which are increasingly important in AI applications.
Key Takeaways
- Introduces reward-weighted fine-tuning for offline RL in LLMs.
- Demonstrates improved performance in question-answering tasks.
- Reduces complexity by eliminating additional hyper-parameters.
- Empirical results show gains in both optimized rewards and language quality.
- Offers a practical solution for enhancing conversational AI capabilities.
Computer Science > Computation and Language arXiv:2506.06964 (cs) [Submitted on 8 Jun 2025 (v1), last revised 16 Feb 2026 (this version, v3)] Title:Offline RL by Reward-Weighted Fine-Tuning for Conversation Optimization Authors:Subhojyoti Mukherjee, Viet Dac Lai, Raghavendra Addanki, Ryan Rossi, Seunghyun Yoon, Trung Bui, Anup Rao, Jayakumar Subramanian, Branislav Kveton View a PDF of the paper titled Offline RL by Reward-Weighted Fine-Tuning for Conversation Optimization, by Subhojyoti Mukherjee and 8 other authors View PDF HTML (experimental) Abstract:Offline reinforcement learning (RL) is a variant of RL where the policy is learned from a previously collected dataset of trajectories and rewards. In our work, we propose a practical approach to offline RL with large language models (LLMs). We recast the problem as reward-weighted fine-tuning, which can be solved using similar techniques to supervised fine-tuning (SFT). To showcase the value of our approach, we apply it to learning short-horizon question-answering policies of a fixed length, where the agent reasons about potential answers or asks clarifying questions. Our work stands in a stark contrast to state-of-the-art methods in this domain, based on SFT and direct preference optimization, which have additional hyper-parameters and do not directly optimize for rewards. We compare to them empirically, and report major gains in both optimized rewards and language quality. Comments: Subjects: Computation and Language (cs...
