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..
MoRAgent: Parameter Efficient Agent Tuning with Mixture-of-Roles
Authors: Jing Han, Binwei Yan, Tianyu Guo, Zheyuan Bai, Mengyu Zheng, Hanting Chen, Ying Nie
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments and thorough ablation studies on various LLMs and agent benchmarks, demonstrating the effectiveness of the proposed method. |
| Researcher Affiliation | Collaboration | 1School of Artificial Intelligence, Beijing University of Posts and Telecommunications 2Huawei Noah s Ark Lab. Correspondence to: Ying Nie <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Fine-tuning LLM with Mo R for agent tasks. |
| Open Source Code | Yes | This project is publicly available at https://mor-agent.github.io/. |
| Open Datasets | Yes | We adopt the publicly available datasets including Tool Bench (Qin et al., 2023), the combination of APIGen (Liu et al., 2024d) and Tool ACE (Liu et al., 2024c) and glaive-function-calling-v2 1, Math Genie (Lu et al., 2024) to fine-tune the corresponding downstream agent tasks respectively. |
| Dataset Splits | Yes | For each role, 80k samples are randomly selected as the training set, while 5k samples are sampled as the validation set. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned in the paper. |
| Experiment Setup | Yes | Also, we set the learning rate to 5e-5, with 4 epochs of fine-tuning by Mo R, and α1 and α2 in Equation 8 set to 1e-3 and 1e-4, respectively. |