AI Coding Live News

The SGLang team released v0.5.22 featuring 950 PRs from 245 contributors: DeepSeek-V4.1 delivers up to 2.3x more throughput per GPU on NVIDIA H200 and 1.9x on AMD MI350X/MI355X via DSpark. The new InstantTensor loader cuts model loading time by 10x. B200 decodes Qwen3.8-Flash-Next 12% faster, while B300 speeds up GLM-5.3-Flash by 10% and yields 1.85x decode throughput for MiMo-V2.6-Pro. SGLang also adds native Apple Silicon PyTorch support and ComfyUI integration.

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Reinforcement learning (RL) pipelines for code agents typically rely on binary test suite outcomes for reward assignment. Under standard Group Relative Policy Optimization (GRPO), all test-passing rollouts within a sampled group receive identical advantage values, completely ignoring code quality, extraneous diff modifications, and algorithmic elegance. This induces severe trajectory bloat and training instability. Researchers introduce GAGAR (Groupwise Agentic Grading and Advantage Redistribution, arXiv:2609.32577). In a shared workspace, an agentic evaluator jointly inspects all passing candidates in a group, ranking them by architectural clarity and minimal intervention. A sum-preserving redistribution reallocates advantage weights toward superior implementations. Validated at industrial scale on MiMo-V2.6-Flash (310B) and MiMo-V2.6-Pro (1.02T parameters), GAGAR curtails trajectory length explosion while boosting code agent benchmark performance and optimization stability.

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@Zaddyzaddy published @BugBunny_ai VulnPR-100 results: on 100 PRs with known vulns (up to one hour each), MiMo-V2.6-Pro found 46 for $8.27 — best open-weight score, matching GPT-6 Astra at roughly 22× lower review cost. MiMo-V2.6-Flash found 41 for $3.47. That is about $0.18 per vuln found for Pro and $0.08 for Flash.

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@arena reports Xiaomi MiMo-V2.6-Pro debuted on Code Arena: WebDev around #10 overall and about #3 among MIT-licensed open-weights models, scoring 1628 AutoEval points—tying Claude Fable 5 (High) and just ahead of Hy4-preview (1624). That is a +153 jump from MiMo-V2.5-Pro (1475). Live human votes are still coming in.

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@ArtificialAnlys reports Xiaomi released open-weights MiMo-V2.6-Pro: Artificial Analysis Intelligence Index 46 (up from 26 on V2.5-Pro), now the top open-weights model, landing on the intelligence-vs-cost Pareto frontier at about $0.13 per Index task with unchanged token prices ($0.435/M input with 99% cache-hit discount, $0.87/M output). It is a 1.02T-total / 42B-active MoE.

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