On July 26, Kuaishou's KwaiKAT team released KAT-Coder-V2.5, scoring 94.9 on PinchBench under a unified Claude Code harness — the first time it edged out Claude Opus 4.8 (93.5) — and simultaneously pushed a 35B-total / 3B-active MoE variant, KAT-Coder-V2.5-Dev, onto Hugging Face under Apache-2.0. But more worth dissecting than the leaderboard win is the engineering overhaul underneath the training stack. KwaiKAT originally blamed the slow RL curve on the algorithm itself, but an audit found that 16% of trajectory failures came from the sandbox, not the policy. Sandbox images were filling disks to 95%, making timeout-driven invalid rollouts account for 6%–7%; remote-sandbox initialization env-var mismatches cost another 6%–7% of reward; and the Gateway Server's chat endpoint introduced 40% token drift at the ~200-turn scale. After these three infrastructure fixes stacked up, the sandbox feedback-error rate dropped from 16% to under 2%, and training collapses shrank by an order of magnitude. Underpinning this layer is AutoBuilder: it pushed the executable-environment build success rate from 16.5% to 57.2%, stacking 100,000+ verifiable repository tasks across 12 languages, each as a "golden patch + test patch + auto-generated three-segment description" triplet. Verification doesn't read exit codes — it parses structured test output and requires ≥90% of the expected tests to reproduce across multiple runs. The data flywheel doesn't just filter "eventually pass" trajectories — it adds process-level hints to "near-pass" tasks, pushing 0-pass subsets back to ~20% pass rates, then washes away leaks via hint-free replay. The base of the open-source KAT-Coder-V2.5-Dev is Qwen3.6-35B-A3B with 127K SFT and RL post-training. This means agentic coding capability can now run, in a 35B-total / 3B-active MoE form, in 4-bit quantization on a single card — and the Qwen open-source ecosystem has another piece of the puzzle. The next lap of agentic coding won't be won by model size — it'll be won by sandbox stability, data hygiene, and reward hacking resistance. KAT-Coder-V2.5's real selling point isn't that one PinchBench point — it's rewriting RL training from "an algorithm problem" into "an infrastructure problem".