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..
Safe RLHF: Safe Reinforcement Learning from Human Feedback
Authors: Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through a three-round fine-tuning using Safe RLHF, we demonstrate a superior ability to mitigate harmful responses while enhancing performance compared to existing algorithms. Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with collected human preferences, significantly improving its helpfulness and harmlessness according to human evaluations. |
| Researcher Affiliation | Academia | 1Center for AI Safety and Governance, Institute for AI, Peking University 2School of Computer Science, Peking University |
| Pseudocode | No | The paper describes the methods using equations and textual explanations, but it does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/PKU-Alignment/safe-rlhf. |
| Open Datasets | Yes | In the first iteration, our prompts were derived from open-source safety-related datasets referenced in Ganguli et al. (2022) and Sun et al. (2023a). |
| Dataset Splits | No | For both the reward model and cost model, the model selection primarily aims to achieve higher prediction accuracy. For different parameter training outcomes, we evaluate their predictive accuracy on a reserved test set and select the one with the highest accuracy for the next step. |
| Hardware Specification | Yes | All experiments in this paper utilized a large language model with 7 billion parameters. The server s CPU was an Intel(R) Xeon(R) Platinum 8378A CPU @ 3.00GHz with 64 cores, and the graphics cards were NVIDIA A800-SXM4-80GB 8, with NVLink support and the graphics driver version being 525.125.06. |
| Software Dependencies | Yes | the graphics driver version being 525.125.06. |
| Experiment Setup | Yes | The hyper-parameters utilized during the Safe RLHF training process are enumerated in Tables 2, 3, and 4. |