Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Regressing the Relative Future: Efficient Policy Optimization for Multi-turn RLHF
Authors: Zhaolin Gao, Wenhao Zhan, Jonathan Chang, Gokul Swamy, Kianté Brantley, Jason Lee, Wen Sun
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we evaluate our algorithm by using Llama-3.1-70B-it to simulate a user in conversation with our model. REFUEL consistently outperforms stateof-the-art methods such as DPO and REBEL across various settings. |
| Researcher Affiliation | Collaboration | Zhaolin Gao1, Wenhao Zhan2, Jonathan D. Chang3 , Gokul Swamy4, Kiant e Brantley5, Jason D. Lee2, Wen Sun1 1 Cornell University, 2 Princeton University, 3 Databricks Mosaic Research, 4 Carnegie Mellon University, 5 Harvard University |
| Pseudocode | Yes | Algorithm 1 REgressing the RElative FUtur E for reinforcement Learning (REFUEL) |
| Open Source Code | Yes | Implementation of REFUEL can be found at https://github.com/Zhaolin Gao/REFUEL/, and models trained by REFUEL can be found at https://huggingface.co/Cornell-AGI. |
| Open Datasets | Yes | We evaluate REFUEL on Ultra Interact (Yuan et al., 2024), which involves the model responding to instructions with complex reasoning tasks, covering general chat scenarios. |
| Dataset Splits | Yes | Dataset % in Dataset Train/Val/Test Max Generation Length H=1 H=2 H=3 H=4 H=5 LT-OFFLINE 76.9 12.1 6.40 3.20 1.40 205K/500/500 1024 |
| Hardware Specification | Yes | The experiments are trained on 8 H100 GPUs for two hours for each iteration. |
| Software Dependencies | Yes | We perform full parameter training for Llama-3-8B-Instruct2. For Armo RM3, we directly use the reward scores without any normalizations. |
| Experiment Setup | Yes | Parameter Setting (Setting One) Method Parameters RLOO-LT-OFFLINE batch size: 128 weight decay: 1e-6 learning rate: 3e-7 schedule: cosine decay warmup ratio: 0.1 |