🇨🇳 Another good model from China.

A 35B agent model claims 1T-model performance by thinking longer, not growing bigger.

Apache-2.0 license, model weights are on Hugging Face.

The technique is proposing a cheaper way to make strong AI agents: teach them longer verified work habits, not just make them bigger.

The paper’s main idea is to make the agent practice long tasks where it searches, uses tools, reads results, fixes mistakes, and checks answers.

The authors build training data from long action records, with an average length of 45K tokens, so the model learns the whole work process.

They then train specialist teacher models for search, science, instruction following, tool use, and other areas, and transfer those skills into 1 student model.

Agents-A1 does very well across long-task benchmarks, including search, science, coding, tool use, and instruction following.