StartLux Interview: A 27B Local Model Rivals DeepSeek Giants via AutoResearch and Local RSI

机器之心 · wechat · 2026-09-16

• Context: 25 Fields medalists published a letter against AI labs benchmark-gaming famous math problems, while OpenAI claimed an internal model with 10,000 agents partially proved the Navier-Stokes problem in 88 hours. In CAICT's report, StartLux's 27B local model scored 39.25 on the MCP benchmark, ranking second—beating 284B DeepSeek-V4-Flash and within 1 point of far larger models.
• Key numbers: built on Qwen3.6-27B with essentially unchanged architecture, it gains +5.34 points entirely from post-training; 70% of the research pipeline involves AI (over 95% if only mechanical execution counts).
• AutoResearch method: AI models diagnose failures, generate improvement hypotheses, auto-build training data, launch training, rerun evals, and check for regressions; humans keep research goals, eval criteria, and key tradeoffs. The team stresses heterogeneous multi-model collaboration rather than distillation—"ground truth" comes from real environments and independent evals.
• Quantization findings: across 1,209 tasks on four benchmarks, Q4/Q6/Q8 stay within -0.1+0.2 points of BF16 for Qwen (Q2 drops 1.3); the 16GB-VRAM feasibility comes from joint quantization-plus-inference optimization.
• Capability reallocation: one agent-focused post-training round lifted GPQA from 83.33 to 90.40 while GAIA fell from 57.57 to 45.70. The CTO argues exploration freedom and self-modification permission must be separated, and that local models—not a single strongest model—are the path to AGI.

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