Xiaojie Cai

Recursive self-improvement had only been closed on post-training, where each trial costs GPU-hours and returns a verifiable score; a frontier pre-training run costs hundreds of thousands of GPU-hours, cannot be repeated, and has no single-score objective. A newly formed team at Shanghai Innovation Institute and Xinxu AI split pre-training into smaller closable loops, using agents for debugging, tiny-SFT and related measures to estimate post-training headroom, and quantified workflows for data tasks, producing the 30B-A3B base model 801 in three months. On 13 base benchmarks it averages 73.13, behind only the larger Qwen3.5-35B-A3B at 76.67.

Recursive self-improvement had only been closed on post-training, where each trial costs GPU-hours and returns a verifiable score; a frontier pre-training run costs hundreds of thousands of GPU-hours, cannot be repeated, and has no single-score objective. A newly formed team at Shanghai Innovation Institute and Xinxu AI split pre-training into smaller closable loops, using agents for debugging, tiny-SFT and related measures to estimate post-training headroom, and quantified workflows for data tasks, producing the 30B-A3B base model 801 in three months. On 13 base benchmarks it averages 73.13, behind only the larger Qwen3.5-35B-A3B at 76.67.