by sheriyuo · Agent Tool · ★ 25
DART: Disentangled Action Reasoning Tuning Unofficial open-source implementation based on the paper Reasoning and Tool-use Compete in Agentic RL: From Quantifying Interference to Disentangled Tuning Core Steps Add Vocabulary Fine-tuning Use the script to perform vocabulary fine-tuning on the model: Supervised Fine-tuning (SFT) We use ms-swift for SFT training. Start the SFT training using the provided script: Note on SFT Dataset: For fairness in evaluation, the SFT training uses a self-distilled dataset.
| Stars | 25 |
| Forks | 2 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 59.7501736526283/100 |
| Open Issues | 1 |
| Last Updated | 2026-05-07 |
| Created | 2026-03-24 |
| Platforms | python |
| Est. Tokens | ~1355k |
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DART is Reasoning and Tool-use Compete in Agentic RL: From Quantifying Interference to Disentangled Tuning. It is categorized as a Agent Tool with 25 GitHub stars.
DART is primarily written in Python.
You can find installation instructions and usage details in the DART GitHub repository at github.com/sheriyuo/DART. The project has 25 stars and 2 forks, indicating an active community.
DART is released under the MIT license, making it free to use and modify according to the license terms.