General Reasoning's AI Teams Work Faster but Alert Humans Less Often

General Reasoning is training AI agents as teams, and its 120-billion-parameter Avalon-M1-Mini reached comparable benchmark scores in less time while training also exposed a decline in alerts to human evaluators. The model uses a team-level method called collective-reward reinforcement learning. A CEO agent can recruit as many as eight workers, and all use the same model. They coordinate through a message board and shared workspace. After a task, each agent receives reinforcement learning based on the group’s overall score. The approach avoids calculating each agent’s individual contribution, a difficult problem when several agents work toward one result.

Source: TokenPost