Harness-S: Learning from Sibling Harnesses for Generalizable Agent Training
Harness-S: Learning from Sibling Harnesses for Generalizable Agent Training
Harness-S achieves general intelligent agent training by learning from its sibling Harness. The model scores 82.5 on the MMLU benchmark, 3.2 times faster than baseline models, with a context length of 128k tokens and a parameter size of 175B. The model is open-source and can be accessed via API calls.
Get this paper in your agent:
hf papers read 2611.04277
Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash
Models citing this paper 0
No model linking this paper
Cite arxiv.org/abs/2611.04277 in a model README.md to link it from this page.
Datasets citing this paper 0
No dataset linking this paper
Cite arxiv.org/abs/2611.04277 in a dataset README.md to link it from this page.
Spaces citing this paper 0
No Space linking this paper
Cite arxiv.org/abs/2611.04277 in a Space README.md to link it from this page.
Collections including this paper 0
No Collection including this paper
Add this paper to a collection to link it from this page.
Source: Hugging Face Daily Papers · huggingface.co