Training/Inference Consistency

The MindStudio skills below own collection and analysis. Read them in order; this skill connects their inputs and outputs without repeating their procedures.

Prerequisite — MSProbe

MSProbe (mindstudio-probe) is required for data collection. If missing, identify the Python environment used for the user's verl-omni task and install it using that environment's package manager and workflow (e.g., uv or pip).

Step 0 — Screen a previous run, if provided

If the user provides an experiment directory or logs, check the saved config for calculate_log_probs=true and inspect that run's rollout_corr/* metrics for initial evidence of differences. Record the source and affected steps/timesteps. Missing metrics or bypassed actor log-prob recomputation is inconclusive. Without previous artifacts, proceed directly to collection.

Step 1 — Load the skills

Prefer installed skills or an existing local msagent checkout. Read each SKILL.md; if missing, fetch the complete skill directory and referenced resources from the linked repository, preserving scripts/, references/, and relative paths. Record the source/version used. If unavailable, report what is missing rather than reconstructing the procedure from memory.

Step 2 — Collect paired dumps

Follow the collection skill using the user's launch script and current verl-omni source. Collect full tensor data for both sides within the diagnostic window, using Step 0's findings, if available, to guide reproduction and fine-grained analysis. Establish sample pairing through correlation logs, not aggregate metrics. Produce the diagnostic wrapper, both dumps, and correlation logs. Existing artifacts may be reused after passing that skill's checks; if either side is missing, fix collection and rerun.

Step 3 — Pair and analyze

Confirm the dumps represent the same sample and computation, with comparable inputs and weights. Pass the paired dumps, correlation evidence, and layout differences to the analysis skill. Follow its module-mapping and script workflow, then interpret the results against current source. Statistics support screening; elementwise conclusions require tensor evidence.

Step 4 — Deliver the report

Provide actual paths to the diagnostic script, dumps, module mapping, and output_5_root_cause_report.md. Include the screening metrics and diagnostic config changes. Explain the pairing, evidence, limitations, and next steps. If execution is unavailable, complete the feasible integration work and identify unverified steps without claiming collection or analysis succeeded.