Equating command success with recovery
A program can run while updating parameters with unintended gradients. Validate the objective contract.
Controlled replay case study
An optimizer fix that stopped crashing but did not yet train correctly
Controlled replay on October 7, 2026: one RTX 4090, PyTorch 2.5.1 and CUDA 12.1. This reproduces a historical public mechanism, not an unseen customer benchmark or physical multi-GPU acceptance.
Denpex recognized weights changing before a pending backward pass. The minimal reorder stopped the exception, but the critic also received generator-objective gradients. A reviewed correction needed explicit parameter ownership and objective isolation before recovery passed.
Reviewed . Reference guidance is not a diagnosis of your workload.
Failure, recommendation and recovery record
- 1
Original failure and initial cause
The reduced workload updated parameters that a pending backward pass still needed. PyTorch reported an in-place version conflict. The initial diagnosis identified that mechanism, but the application objective contract was needed to judge a complete remedy.
- 2
Freeze the initial recommendation
The initial prediction was saved before comparing the recovery result. Prediction SHA256: afcbf450d732cd3aae2d9b966f94ffccfba45978fd9065b3fd21d64fbdd92c09. Reference seal: 6bb3cf7638f5de8728eac31dfff6de0b8dc47a705f319b1b7365948c01bec48b. These hashes identify the recorded replay artifacts; they do not prove the model had never encountered a public solution.
- 3
Apply the minimal change and check intent
Delaying the updates removed the version exception. The independent critic-gradient check failed because it included another objective. This was recorded as an unsuccessful complete repair, even though the process could run.
- 4
Reinvestigate with the missing application facts
An operator supplied parameter ownership, zeroing order and backward scope. The follow-up stayed in the same incident and conversation, retaining the original report. The reviewed action isolated each objective to its intended parameters before applying updates. That supplied evidence was not automatic inspection of arbitrary training internals.
- 5
Verify the reviewed correction against a reference
Three runs with three operations each passed finite loss, finite gradients, intended parameter updates and directly calculated matrix-gradient comparisons independent of autograd. These nine operations establish the reviewed reduced case. The initial answer is not retroactively credited as independently complete.
Cause, action and recovery are separate results
| Signal | What it means | Next action |
|---|---|---|
| Optimizer stepped before pending backward | The initial mechanism was identified correctly. | Delay mutations until required gradients exist. |
| Version exception disappeared | Execution advanced, but training intent remained unverified. | Compare each objective gradient with its owned parameters. |
| Critic received extra objective gradients | The minimal action was an incomplete remedy. | Preserve the failed attempt and supply ownership evidence for reinvestigation. |
| Reviewed isolated gradients passed | The reduced workload matched its mathematical reference. | Keep customer objective, checkpoint and recurrence checks separate. |
Evidence checklist
- Parameter ownership for each optimizer
- Objective and backward/zeroing order
- Frozen first answer and retained failed attempt
- Independent intended-gradient reference
- Finite loss and intended updates across three bounded runs
Common mistakes
Counting the reviewed correction as a perfect first answer
The reviewed fix incorporated extra operator facts and manual assessment. Report that assistance explicitly.
Tools and deeper references
Download the public replay receipt (JSON)
Retained initial defects, reviewed checks, frozen artifact hashes and proof limits.
Layout replay and instruction defect
See the companion controlled case and its proof limits.
Operation-specific evidence
Gather the fact that changes the decision instead of a generic bundle.
Capture connected workload incidents
Keep failure, evidence and recovery state distinct.
Frequently asked questions
Did the first recommendation completely fix training?
No. It identified the mechanism, but the minimal reorder failed the intended-gradient check. Reviewed objective isolation and added ownership evidence were needed.
What remains outside the recovery proof?
Customer convergence, long-term recurrence, checkpoint correctness and physical multi-GPU behavior. This is a bounded single-GPU replay, not a universal remedy accuracy score.
Apply the method to your incident
Use the three free diagnoses to review your error and relevant evidence. Keep reference guidance separate from the cause and recovery status of your own workload.
Diagnose your incident