Sakana AI发布多智能体自监督技术MASS
Sakana AI的MASS让模型通过团队协作自我提升，在开放研究任务中效果显著提升。
Sakana AI与加州大学伯克利分校合作推出多智能体自监督(MASS)技术。该技术使用27B参数开源模型，在四个研究基准测试中，第二周期后每输出token得分提升1.2-1.6倍。MASS通过虚拟子团队协作、优化工作流选择和集体经验蒸馏实现自我监督。
Introducing Multi-Agent Self-Supervision (MASS): Recursive self-improvement through collective intelligence
Blog: https://t.co/LkQe5k7jOW
As AI systems tackle increasingly open-ended research problems, human experts may struggle to assess results and guide further progress. The model itself may be the best available optimizer and evaluator. The challenge is obtaining useful self-supervision within that homogeneous loop, where a model risks reinforcing its own blind spots rather than correcting them.
We ask whether a model can learn from the collective intelligence of its own team.
We introduce Multi-Agent Self-Supervision (MASS). One shared model solves tasks through a team of virtual subagents, searches for better multi-agent workflows, then uses its own judgments to select the best ones.
Training on the best team’s executions provides self-supervision by distilling the team’s collective experience into the shared model.
We ran two cycles using a 27B open-weights model on synthetic open-ended research tasks. After the second cycle, score per output token reached 1.2-1.6x the base model's level across four research benchmarks.
Three key results:
First, MASS enables role generalization within an RSI loop: training only on task-solving data from the optimizee naturally improves the capabilities of both the optimizer and the evaluator.
Second, multi-agent trajectories are more efficient training data than single-agent trajectories for RSI.
Third, optimizer capacity, rather than evaluator capacity, can be the bottleneck in the speed of improvement.
We believe the community needs to investigate homogeneous RSI carefully to support AI safety. When a model is also its own evaluator, shared blind spots could allow mistakes to be accepted as progress and reinforced through training. For AI to improve beyond human expertise safely, we need to understand these failures and preserve effective ways to detect them and intervene.
Paper: https://t.co/SzXDlAzrUO Code: https://t.co/xc870ZHRgh
This work was a collaboration with researchers at UC Berkeley.
