当题库追不上模型，AI开始给自己出题：中国这支团队跑通了数据层RSI
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Merged summary
TL;DR - Chinese team "无尽前沿" (SJTU AI School, DP Technology, Shanghai Institute for Algorithm Innovation) released BigBang-V1, an open-sourced 35B MoE base model (~3B active, 262K context) claimed to be the first trained natively via recursive self-improving, with 100% AI-synthesized post-training data. It matters because it targets the training-data bottleneck rather than compute scaling.
- Data-layer RSI loop: a Generator Agent (a code agent that edits/runs/debugs the data-synthesis program itself, picking domains, reasoning-chain length, and tools) pairs with an adversarial Critic Agent that checks format/execution/completeness, then correctness, verifiability, difficulty, diversity, and training value.
- Two-level co-evolution: the fast inner Generator–Critic loop is guarded by an outer loop that trains models on competing pipeline variants and evaluates on held-out real research tasks, back-calibrating the Critic when high-scored data fails to improve models — the stated defense against reward hacking and synthetic-data collapse.
- Task selection is grounded in "frontier + verifiable" science (formal methods, code execution, numerics, simulators, domain tools), so the task pool doesn't saturate like a static benchmark bank.
- Reported results (per the team's tech report, not independently verified): 10 first-place finishes among 35B models; claimed to beat 1T-scale DeepSeek V4 Pro Preview on FrontierScience Research and PaperBench; BrowseComp 76.5, SWE-Bench Pro 54.2; case studies include a 47bp transposon insertion localization and a paper-reproduction run scoring 0.7657 (331/485 points). Weights on Hugging Face, code on GitHub.
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当题库追不上模型，AI开始给自己出题：中国这支团队跑通了数据层RSI
TL;DR - Chinese team "无尽前沿" (SJTU AI School, DP Technology, Shanghai Institute for Algorithm Innovation) released BigBang-V1, an open-sourced 35B MoE base model (~3B active, 262K context) claimed to be the first trained natively via recursive self-improving, with 100% AI-synthesized post-training data. It matters because it targets the training-data bottleneck rather than compute scaling.
- Data-layer RSI loop: a Generator Agent (a code agent that edits/runs/debugs the data-synthesis program itself, picking domains, reasoning-chain length, and tools) pairs with an adversarial Critic Agent that checks format/execution/completeness, then correctness, verifiability, difficulty, diversity, and training value.
- Two-level co-evolution: the fast inner Generator–Critic loop is guarded by an outer loop that trains models on competing pipeline variants and evaluates on held-out real research tasks, back-calibrating the Critic when high-scored data fails to improve models — the stated defense against reward hacking and synthetic-data collapse.
- Task selection is grounded in "frontier + verifiable" science (formal methods, code execution, numerics, simulators, domain tools), so the task pool doesn't saturate like a static benchmark bank.
- Reported results (per the team's tech report, not independently verified): 10 first-place finishes among 35B models; claimed to beat 1T-scale DeepSeek V4 Pro Preview on FrontierScience Research and PaperBench; BrowseComp 76.5, SWE-Bench Pro 54.2; case studies include a 47bp transposon insertion localization and a paper-reproduction run scoring 0.7657 (331/485 points). Weights on Hugging Face, code on GitHub.