What mattered
HF-native training and portable checkpoints, independent expert/context/tensor/data parallelism, faithful multi-turn trajectories, operation-specific model support, and explicit resume/export/serving boundaries make the single-machine-to-cluster transition inspectable.
What changed locally
Halo informed completed Issues #159, #161 and #162: trajectory export preserves tool actions and observations with per-turn supervision; compatibility reports distinguish static predictions from matching device receipts; artifact lifecycle contracts bind identity, history, runtimes and legal next actions. The 2026-10-02 source-pinned review connects these implementations to the original contracts.
Evidence limits
The documented runtime requires NVIDIA/CUDA containers, not MLX or Metal. Halo has not been run on this Mac or adopted as a dependency, and its B300 benchmarks have not been reproduced. Sampled-token RL, asynchronous weight synchronization and distributed training were not implemented locally; its license includes supplemental commercial and attribution terms.
Retained disposition
Retain the completed contract improvements and scale-boundary reference. No experiment is active. A future runtime or algorithm trial requires an owner-opened factory question, frozen baseline and held-out evaluator, regression gate, compatible hardware and fixed resource budget.
The action is retained as study context rather than an active backlog item. Any implementation, download, training run, or benchmark requires a fresh scoped question under the repository's experiment gate.
How to read this dossier
This canonical page separates the retained repository decision from the material that informed it. A source may describe an external claim, historical measurement, local observation, or planned procedure; those evidence classes are not interchangeable. Follow the provenance links before reusing a number or method.
Status and disposition describe what PostTrainLLM retained when this record was indexed. They are not a live product promise, a newly run benchmark, or permission to restart historical work. Unknown or unmeasured fields remain unknown rather than being treated as zero.
To reuse the record, first name the exact claim you need and trace it to the linked source. Then check whether the original environment, model revision, data split, evaluator, hardware, and budget match the proposed use. If they do not, treat the record as a hypothesis or design reference and run the smallest fresh comparison that can falsify it. Preserve negative outcomes and regressions beside any improvement; a local win on one slice does not silently become a general capability claim.
Source provenance
The normalized record comes from docs/studies/registry.json. The links below are the tracked evidence and explanatory sources preserved with the record.