Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Stepwise Alignment for Constrained Language Model Policy Optimization
Authors: Akifumi Wachi, Thien Tran, Rei Sato, Takumi Tanabe, Youhei Akimoto
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 45,788 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,791 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results show that SACPO can fine-tune Alpaca-7B better than the state-of-the-art method in terms of both helpfulness and harmlessness. |
| Researcher Affiliation | Collaboration | LY Corporation University of Tsukuba RIKEN AIP EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 Stepwise Alignment for Constrained Policy Optimization (SACPO) |
| Open Source Code | Yes | Code and models are available at https://github.com/line/sacpo. |
| Open Datasets | Yes | We utilize the PKU-Safe RLHF preference dataset [25] with more than 30,000 expert evaluations. |
| Dataset Splits | Yes | Table 1: Hyper-parameters used in the two stages of our experiment. |
| Hardware Specification | Yes | Our experiments were conducted in a workstation with Intel(R) Xeon(R) Silver 4316 CPUs@2.30GHz and 8 NVIDIA A100-SXM4-80GB GPUs. |
| Software Dependencies | No | We use TRL [47] for implementing DPO and KTO. |
| Experiment Setup | Yes | The hyper-parameters used in our experiment for helpfulness and safety (i.e., harmlessness) are summarized in Table 1. |