AI开始改进“改进自己的方法”，RSI进入平方时代丨MetaRSI
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TL;DR - CosmosMind and university collaborators introduced MetaRSI-v1, a meta-recursive architecture that jointly coordinates improvements to model weights, training data, and agent harnesses. It matters because the system aims to optimize not only an AI system but also the strategy used for its continued self-improvement.
- MetaRSI-v1 unifies Data-RSI, Harness-RSI, and Model-RSI through a shared feedback-and-verification “Loop Kernel.”
- Its agents optimize both the sequence of improvement operators and each operator’s internal rules, adding a meta-layer that adapts the overall improvement strategy.
- Without an external teacher model, a Qwen3.5-35B-A3B model reportedly gained an average 10.9 points across four benchmarks.
- Data- and Harness-RSI reportedly improved six frontier models by an average 7.3 points on Terminal-Bench 2.1; the team also released its RSI-Harness project as open source.
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AI开始改进“改进自己的方法”，RSI进入平方时代丨MetaRSI
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TL;DR - CosmosMind and university collaborators introduced MetaRSI-v1, a meta-recursive architecture that jointly coordinates improvements to model weights, training data, and agent harnesses. It matters because the system aims to optimize not only an AI system but also the strategy used for its continued self-improvement.
- MetaRSI-v1 unifies Data-RSI, Harness-RSI, and Model-RSI through a shared feedback-and-verification “Loop Kernel.”
- Its agents optimize both the sequence of improvement operators and each operator’s internal rules, adding a meta-layer that adapts the overall improvement strategy.
- Without an external teacher model, a Qwen3.5-35B-A3B model reportedly gained an average 10.9 points across four benchmarks.
- Data- and Harness-RSI reportedly improved six frontier models by an average 7.3 points on Terminal-Bench 2.1; the team also released its RSI-Harness project as open source.