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`backoff_mean`/`reject_all_mean` 来量: `reject_all_mean ≈ 原全拒次数`(=backoff_mean+brakes_mean,与轮次 33 cap 的 19.55 可比),`backoff_mean/(backoff_mean+brakes_mean)` 就是"缩步能救回的比例"。这是补跑(#22)要读的第一个数。 +## 多段推理的代价实测(B=1、K=8、逐轮交替、奇数轮反序) + +- `docs/experiment_round38_steps_cost.json`(24 轮 × 每轮 40 次,取每档的中位): + + | 段数 | 推理 P50 | 相对 1 段增量 | + |---|---|---| + | 1 | 9.02 ms | — | + | 2 | 14.98 ms | **+5.97 ms** | + | 4 | 26.72 ms | +17.71 ms | + | 8 | 51.07 ms | +42.05 ms | + +- 每多一段 ≈ 多一次解码器前向(编码器 `mem` 只算一次),所以增量近似线性;9.02 ms 这一档比轮次 33 报的 7.2 ms 高,是不同会话的时钟/上下文差别,**只用同批内的差值,不用绝对值跨轮比**。 +- 合到整周期上:轮次 34 的现口径 P50 ≈ 33.5 ms,两段就是 ≈39.5 ms(25 Hz),四段 ≈51 ms(19.6 Hz)——**离 20 ms 预算更远**,所以"靠多段推理把一步的偏差补回来"在部署上不成立,只能作为开环诊断口径。 +- 与 K 这条轴叠加会更糟(K=16 认证点翻倍),所以 `K=8/16 × cert_batch=0/1` 的四格周期微基准排在当前批次里(`ml/runs/_kcost_probe.py`),这是判断"候选数能不能买到部署里的到达率"的关键一列。 + ## 候选为什么会"塌":两段积分把 K 条候选吸成一条(轮次 39 探针,机理定案) - `ml/runs/_cand_div.py`(256 题特征、同一批噪声 z、K=8,只改段数):