The hero run's third week brought two inflection points. A ragged all-to-all backend swap — expected to yield 10% more throughput after last week's merge — hung twice at 11-rack scale and was rolled back to pooled-wave within hours. Multi-day investigation traced the root cause to an NVLink fabric fault on two GB200 domains, alongside two independent software bugs: a step-counter sharding mismatch that duplicated NCCL symmetric-memory registrations, and a QuACK 0.6.1 GEMM scheduler defect reproduced with output corruption. The same NVLink fault struck the pooled-wave hero on September 5, confirming hardware. Meanwhile, Larry Dial documented attention-gate norm growth diverging from the scaling ladder about 1% into training — early-layer gates shrinking to 1.3% of neutral scale — and weight-decay ablations on the H100 ladder showed a small Paloma gain. The hero relaunched from step 58,000 with gate and router weight decay enabled; as of September 6 it stands at 3.23T tokens with train loss 1.228.
The 67B-A2B 10T pretraining campaign closed after completing its full token budget, its Paloma loss well below the 2.269 preregistered target. Long-context skew experiments (2×, 4×, 8× upsampling) all completed on TPU v4-2048, with generative MRCR improving from 87% to 91% at 4×. A TaskTrove quality audit flagged 35% of sampled tasks as broken or hackable, driving four point releases that removed 5,426 invalid tasks. Ahmad Qamar qualified asynchronous RL on Snowball 67B-A2B, completing 25 Megatron updates with 37% less training time. The spec-driven Common Crawl curation run reached 10T pre-dedupe tokens, roughly 15% through its target, while Ricardo Dominguez preregistered a 33% SWE-bench pass@1 prediction for Snowball post-distillation.
The milestone’s pretraining hero runs and their intermediate cooldowns — the concrete use of compute.
Epic title: [Hero Run] ~120B-A8B XT on B200s
Summary: Prepare the next best model for post-training on the path to our EOY 256–500B-AYB run.
The 535B-A23B hero run attempted a backend swap to the ragged all-to-all expert parallelism (EP) transport on September 2, expecting roughly 10% higher throughput and a drop in token assignment losses from ~3% to ~0.01%. #8868 added checkpoint-resume support across run IDs so the new run could pick up from the pooled-wave hero's step-54000 permanent checkpoint. The ragged hero matched the old run's loss step for step and reached 23.4% MFU over 70 clean steps, then hung silently on one rack at step 54071 on the first attempt and step 54001 on the second — no error from NCCL, XLA, CUDA, or Kubernetes, just a fifteen-minute stall until the progress watchdog killed the gang #8870. #8884 rolled the hero back to the pooled-wave transport, and it resumed cleanly.
Matt Wittmann conducted a multi-day investigation into the hangs. An eleven-rack reproduction on September 3 did not hang but revealed a version skew, and barrier instrumentation on September 4 produced the first measured inter-rack asymmetry. A separate bug — the step counter's inferred sharding causing JAX to compile duplicate XLA executables, doubling symmetric-memory arena registrations — was root-caused in #8861 and fixed in #8912's follow-up. The hang itself traced to two leads: a confirmed GEMM scheduler defect in the pinned QuACK 0.6.1 (reproduced with output corruption on GB200s; upstream fix in QuACK 0.6.4), and an NVLink fabric fault — the pooled-wave hero itself stalled on September 5 with an NVLink error originating on one of the three racks the telemetry window had flagged. #8858 now records full launch provenance on #8506 before every deployment.
Larry Dial documented attention-gate norm growth in #8818: at step 42,000 the hero's layer-0 attention gate had decayed to near zero, effectively disabling attention in the first ~20 layers. Layer-skip ablations confirmed that dropping layers 0–9 costs only 0.03 nats of loss.
Percy Liang and Kaiyue Wen discussed whether scale invariance of training statistics should hold across depth, and whether weight decay could interfere with RMSNorm compensation. #8833 adds a linear-annealing weight decay of 0.02 on the attention gate and MoE router, folded into Adam without extra optimizer state. The hero relaunched from step 58,000 with the decay enabled #8890; #8854 pinned a permanent checkpoint at step 55,000 to allow a clean comparison against the no-decay baseline.
Will Held ran the hero's scaling-law Paloma forecast against eleven open-weight base models in #8824. The hero's projected final perplexity of 0.7406 BPB lands between DeepSeek V4 Flash Base (0.7351) and Nemotron 3 Ultra (0.7567), lower than Arcee Trinity Large, Mistral Large 3 675B, OLMo 3, and Seed OSS. Kimi K2 Base (0.6754) and DeepSeek V4 Pro Base (0.6759) sit roughly 9% below the hero forecast.
Percy Liang noted that because decontamination practices of the comparison models are unknown, the hero's advantage may be understated. Separately, #8827 tracks prompt completions across checkpoints as a qualitative sanity check on the run's learning progress.
#8684 made ragged all-to-all the default expert-parallel transport on the pinned XLA-fork PJRT build, landing 737 lines of additions from Matt Wittmann.
#8865 from Ethan Liu aligned the first-experiment tutorial with the llama_nano config, fixing a 2048-vs-512-token sequence-length mismatch that broke the CPU example. #8920 from Brett Jurman separately fixes the README tutorial example. #8938 from Max Freedom Pollard omits empty Markdown link and image titles.
Dependabot patched 46 Python dependency alerts across the root lock #8834, bumped seven npm packages across five directories #8838, and updated mkdocs-material to 9.7.7 for a DOM-based XSS fix in Haliax docs #8888. Fourteen automated external-runtime advances landed MarinSkyRL, Harbor, and Evalchemy upstream commits throughout the week.
Closed milestones: #6706 (B200 MFUs above 20%) and #6143 (port-model-to-flax agent playbook).
External contributors landed eleven pull requests this week across documentation, evaluation, and core training infrastructure. Ethan Liu (Berkeley) joined Discord, found a sequence-length mismatch in the first-experiment tutorial, and shipped the fix in #8865 alongside an eval registry cleanup in #8883. Vuk Rosić introduced himself the same week and contributed an SFT coverage-ratio reduction in #8880. Yizheng Jiao tightened the fused cross-entropy block-size budget in Levanter #8932 and added a non-Blackwell GPU PJRT extra #8931. chiang21fcb corrected the MuonH hyperball norm to operate over matrix dimensions only #8749, and Akshay Pal added a configurable duplicated-data testbed arm #8808.
On Discord, two CMU PhD students working on long-context RL and long-horizon agents reached out about contributing to behavior analysis, leading to a scoping call with the post-training team. Ricardo Dominguez pre-registered a Snowball post-distillation SWE-bench pass@1 prediction from intermediate checkpoints #8933 and proposed extending the method to the hero run. In data curation, a community member opened an experiment proposal on deduplication scaling laws #8806.
Twenty-eight people introduced themselves this week. They span Scale AI (post-training and verifiers), Canva Research (RL scaling), MIT (deep learning theory), Epic Games (coding agents), Roblox (3D foundation models), Shanghai AI Lab (generative modeling), Deep Forest Sciences (chemical LLMs), Mila (data-science benchmarks), Tencent (LLM serving), and several universities including SKKU, HKU, CUHK, Oxford, and Berkeley. Margalit Glasgow (MIT postdoc, deep learning theory and reasoning) intersects with the ongoing hyperparameter-transfer work in the hero run scaling ladder. LDJ, formerly of Nous Research, brings six years of research on training efficiency and generalization. Gabor (pgab), fresh from quant finance, joined the RL thread with references on actor-learner divergence within days of arriving.
The week's shared research leaned toward RL methodology: Russell Power published a curriculum-RL experiment on environment training stability, and MBZUAI released TxT360-v2, a large synthetic-data collection relevant to the data-mixing pipeline.
| Lab / Org | People | PRs | Issues filed | Comments | Discord msgs | Total |
|---|---|---|---|---|---|---|
| 5 | — | 2 | 6 | 24 | 32 | |
| 2 | — | — | — | 17 | 17 | |
| — | — | — | — | — | — | |
| — | — | — | — | — | — |
Completed marin-community/marin_moe runs, grouped by Agent MoE budget. Speedup is relative to the original baseline run for each budget and charges each variant by its actual reported FLOPs. Best observed point is 17.40× from mhep-ladder-hist-20260808c-fsdp-chunk1-d1024.
The 535B-A23B hero run advanced from 1.82T to 3.23T tokens this week, but the path was eventful. The original run hero-12d8b6f0-dee637 reached roughly 2.68T tokens and Paloma macro loss 2.291 (0.732 BPB) before being paused at step 58014 on Sep 3 to free eleven racks for debugging the ragged all-to-all hang #8870. A forced temporary checkpoint captured the state at the cost of about one step. Matt Wittmann then relaunched from that checkpoint as hero-wd-gate-router-p02-step58k #8890, now with gate and router weight decay enabled. That run is currently running on 704 GB200 GPUs at 21.7% MFU, with train loss 1.228 at 3.23T tokens.
The gate/router weight decay was introduced by Larry Dial in #8833, motivated by analysis of attention-gate norm growth at hero scale #8818. At step 42k the per-head attention-gate L2 norms reached 8–10 in early layers, effectively gating attention off in layer 0 and driving a divergence from the scaling ladder trajectory. The decay peaks at 0.02 and anneals linearly to zero over the full run. Scaling-ladder ablations at d1024 showed slightly lower Paloma loss with decay on both gate and router weights.
On Sep 5 the production hero stalled for fifteen minutes and died of an NVLink fabric fault on domain dh1-r137, one of three racks the earlier telemetry window had flagged. Matt Wittmann filed #8934: two NVL72 domains on cw-us-east-08a show fabric-level faults, with one domain accumulating 500+ NVLink recovery events. This is now the leading explanation for the ragged-expert-parallelism (EP) hangs and the strongest evidence that the root cause is hardware, not software. The hero restarted from its hourly checkpoint, losing 125 steps and 20 minutes.
The ragged all-to-all investigation #8870 consumed a substantial debugging effort. Matt Wittmann ran multiple eleven-rack reproduction windows, eliminated data dependence, residency starvation, watch-step aliasing, and checkpoint collectives as causes, and discovered two independent bugs along the way. First, a step-counter sharding bug caused JAX to compile two train-step executables, each registering its own NCCL symmetric-memory window over the same arena — the root cause of the capacity-limited eval crash in #8861, fixed in #8911. Second, the deployed QuACK 0.6.1 had a GEMM scheduler defect that left output regions unwritten under contention — reproduced on four GB200s and fixed upstream in QuACK 0.6.4. Despite these finds, the hero was rolled back to the pooled-wave transport #8884, which costs 0.16 MFU (22.71% vs 22.87%) but drops 2.67% of routing assignments versus 0.018% on ragged.
Will Held closed the open-weight perplexity comparison #8824 after evaluating fifteen base models. The hero's final Paloma forecast is 0.7406 BPB, putting it just above DeepSeek V4 Flash (0.7351) and well above Nemotron 3 Ultra (0.7567), but below Kimi K2 Base (0.6754) and DeepSeek V4 Pro Base (0.6759). The forecast is based on fits of BPB against dropless Paloma macro loss across 77 ladder evaluations.
Preregistration: Ricardo Dominguez preregistered Snowball post-distillation SWE-bench pass@1 at 33% #8933. The prediction uses a multiplicative scaling law relating pre-training steps and SFT budget to BPB on teacher trajectories, then mapping BPB to pass@1 via the Grug fine-tune series.
Most of the other W&B runs this week were short diagnostic and debugging runs. The ra2a-* runs (all crashed) were one-rack reproductions for the ragged-EP investigation. The b8753 and cor-j* runs (all finished) were correctness and comparison tests at one-rack scale. The mx09012328d1narrowfix run (crashed) was a narrow-fix diagnostic.
| Run | User | Hardware(?) | Hours(?) | FLOP Budget(?) | Loss | BPB(?) |
|---|---|---|---|---|---|---|
| #8870 ra2a-eval-nccl2312-8870-c13d1-r2 | Matt Wittmann |
NVIDIA GB200 (64 chips) |
0.2h |
3.01e22 model
2.51e24 HW (1%) |
— | |
| #8833 #8890 hero-wd-gate-router-p02-step58k | Matt Wittmann |
NVIDIA GB200 (704 chips) |
2.8d |
4.84e23 model
2.23e24 HW (22%) |
— | |
| #8870 ra2a-rt2-c13d1-48k | Matt Wittmann |
NVIDIA GB200 (64 chips) |
0.2h |
3.01e22 model
1.92e24 HW (2%) |
— | |
| #8870 ra2a-evalinfo-c13d1 | Matt Wittmann |
NVIDIA GB200 (64 chips) |
0.2h |
3.01e22 model
1.92e24 HW (2%) |
— | |
| #8870 ra2a-evalnccl-c13d1 | Matt Wittmann |
NVIDIA GB200 (64 chips) |
0.2h |
3.01e22 model
1.91e24 HW (2%) |
— | |
| #8824 hero-12d8b6f0-dee637 | Rafal Wojdyla |
NVIDIA GB200 (704 chips) |
14.3d |
4.01e23 model
1.88e24 HW (21%) |
BPB: 0.732 | |
| #8870 #8868 hero-ragged_a2a-ep-step54k | Matt Wittmann |
NVIDIA GB200 (704 chips) |
0.9h |
3.74e23 model
1.59e24 HW (23%) |
— | |
| cor-j1-triple | Matt Wittmann |
NVIDIA GB200 (64 chips) |
0.2h |
1.88e22 model
1.34e24 HW (1%) |
— | |
| cor-j1b-triple | Matt Wittmann |
NVIDIA GB200 (64 chips) |
0.2h |
1.88e22 model
1.17e24 HW (2%) |
— | |
| cor-j4-chunk1 | Matt Wittmann |
NVIDIA GB200 (64 chips) |
0.1h |
1.88e22 model
9.99e23 HW (2%) |
— | |
| #8870 mx09012328d1narrowfix | Matt Wittmann |
NVIDIA GB200 (64 chips) |
0.3h |
2.64e22 model
1.47e23 HW (18%) |
— | |
| #8870 ra2a-ref8-c13d1-48k | Matt Wittmann |
NVIDIA GB200 (64 chips) |
0.2h |
3.02e22 model
1.33e23 HW (23%) |
— | |
| b8753-hero-1 | Matt Wittmann |
NVIDIA GB200 (64 chips) |
0.6h |
3.02e22 model
1.33e23 HW (23%) |
— | |
| b8753-main-1 | Matt Wittmann |
NVIDIA GB200 (64 chips) |
0.7h |
3.02e22 model
1.31e23 HW (23%) |
— | |
| #8870 hero-wd-1rack-test-48k | Larry Dial |
NVIDIA GB200 (64 chips) |
0.4h |
3.02e22 model
1.31e23 HW (23%) |
— |
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