35B模型科研能力超过 1T 级 DeepSeek V4 Pro：BigBang 跑通RSI数据闭环
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TL;DR - Shanghai Jiao Tong AI Institute, DP Technology, and Shanghai Algorithm Innovation Institute released BigBang-V1, a 35B research-oriented model post-trained on 100% AI-synthesized data, which reportedly beats the much larger DeepSeek V4 Pro Preview on several hard scientific/research benchmarks. It matters as a claimed working demonstration of a recursive self-improvement (RSI) loop at the data layer rather than the model-scaling layer.
- Reported scores: FrontierScience Research 46.2 vs 40.7, HLE 50.3 vs 48.2, BioMysteryBench Human-Difficult 15.7 vs 13.7, PaperBench 53.6 vs 50.4 (BigBang-V1 vs DeepSeek V4 Pro Preview); plus BrowseComp 76.5 and SWE-Bench Pro 54.2, suggesting transfer beyond science.
- The pipeline pairs a Generator Agent (a code agent that edits/runs/debugs the data-synthesis program itself — choosing domains, reasoning-chain length, tools, and verification methods) with an adversarial Critic Agent doing two-tier format and value/difficulty screening.
- An outer loop trains models on data from competing pipeline versions and evaluates on held-out real research tasks, recalibrating the Critic when its scores don't translate into measured capability gains — an explicit guard against reward hacking and Critic-pleasing.
- Case studies: 3/3 stable identification of Norovirus GII.4 from noisy FASTQ data (comparison model 0/3), and a 26-circle packing solution of 2.635983 in ~3.02s vs 2.630957 in ~351.07s, attributed to exploiting hexagonal structural priors with SLSQP restarts.
- Caveat: results are self-reported by the team via a WeChat announcement; humans still define objectives, budgets, held-out task sets, and acceptance criteria.
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35B模型科研能力超过 1T 级 DeepSeek V4 Pro：BigBang 跑通RSI数据闭环
TL;DR - Shanghai Jiao Tong AI Institute, DP Technology, and Shanghai Algorithm Innovation Institute released BigBang-V1, a 35B research-oriented model post-trained on 100% AI-synthesized data, which reportedly beats the much larger DeepSeek V4 Pro Preview on several hard scientific/research benchmarks. It matters as a claimed working demonstration of a recursive self-improvement (RSI) loop at the data layer rather than the model-scaling layer.
- Reported scores: FrontierScience Research 46.2 vs 40.7, HLE 50.3 vs 48.2, BioMysteryBench Human-Difficult 15.7 vs 13.7, PaperBench 53.6 vs 50.4 (BigBang-V1 vs DeepSeek V4 Pro Preview); plus BrowseComp 76.5 and SWE-Bench Pro 54.2, suggesting transfer beyond science.
- The pipeline pairs a Generator Agent (a code agent that edits/runs/debugs the data-synthesis program itself — choosing domains, reasoning-chain length, tools, and verification methods) with an adversarial Critic Agent doing two-tier format and value/difficulty screening.
- An outer loop trains models on data from competing pipeline versions and evaluates on held-out real research tasks, recalibrating the Critic when its scores don't translate into measured capability gains — an explicit guard against reward hacking and Critic-pleasing.
- Case studies: 3/3 stable identification of Norovirus GII.4 from noisy FASTQ data (comparison model 0/3), and a 26-circle packing solution of 2.635983 in ~3.02s vs 2.630957 in ~351.07s, attributed to exploiting hexagonal structural priors with SLSQP restarts.
- Caveat: results are self-reported by the team via a WeChat announcement; humans still define objectives, budgets, held-out task sets, and acceptance criteria.