The reward went up. What changed inside the model?

People specify an outcome. A real training system then executes something else on its behalf: a grader, a verifier, a reward model, or a program. The number it returns may be composed from several parts, sensitive to surface features, or reachable by a route nobody intended.

reward-lens keeps that scoring process inspectable wherever the available access allows, and preserves the components, gates and overrides before they collapse into one number.

In the policy-gradient runs shown here the optimizer never sees the score. It sees an advantage. Grouping, baselines, normalization, masks and clipping all sit between the two, and any of them can change the sign as well as the size of what a rollout contributes.

Keeping the score tree and the run record intact makes the alternative computable: change one component of the reward and the same recorded rollouts can be scored again, without calling the grader, to see which of them would have been reinforced instead.

The optimizer computes local credit and then discards the decomposition, keeping only the summed direction. reward-lens partitions that immediate gradient back across the rollouts that produced it, and checks the partition against an independently computed full update rather than trusting it.

Contributions align, oppose, and cancel. On the recorded step shown here more than half of the per-rollout gradient mass cancels before the optimizer sees any direction at all.

This audits one step at the current parameters. It does not assign long-run responsibility for a behaviour that emerged over a whole run.

Weight distance is not the question. Behaviour and internal features can move differently from each other, and both can move differently from what the training signal selected for. Four quantities are distinct here: the pressure applied, the response the policy could cheaply make, the movement actually observed, and the part of that movement nothing accounts for.

This is the account reward-lens is built to make close or visibly fail. On the run available here it does not close. The budget balances, but it balances on the sampling noise of two small batches, and the test's own detection floor sits hundreds of thousands of times above the effect it is arbitrating. Neither verdict could have failed meaningfully, and that is the honest reading.

On one public labelled run, a variance-derivative signal alarms before the fitted behavioural transition, and the named gradient-norm comparator peaks after it. The interesting part is not the lead. It is the control.

Two signals appeared to lead by more and were both discarded, because on order-destroyed surrogates built from the same series they fire on roughly two thirds of cases. A signal that alarms on noise has not detected anything. Separating those was the work.

One run, one scalar time series. No activations were read and no mechanism was named. An alarm and an explanation are different achievements.