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monad.ai / updateAdaptiveWeights

# Function: updateAdaptiveWeights() ​

> updateAdaptiveWeights(`reward`, `breakdown`, `optionsOrLearningRate?`): `void`

Defined in: kernel/adaptiveWeights.ts:366

Applies one online gradient step to the globally learned weights.

    Δweight = learningRate × reward × contribution

Scorers with high contribution to a good outcome get heavier; scorers that pushed a bad decision get lighter. The update is idempotent with respect to sign: a series of failures will keep driving a weight toward `WEIGHT_MIN` but can never push it below that floor.

NaN and zero rewards are ignored (no-ops).

## Parameters ​

### reward ​

`number`

Continuous reward signal in [−1, 1]; from correlateOutcome.

### breakdown ​

`Record`<`string`, `ScorerBreakdown`>

Per-scorer contributions from the decision being evaluated.

### optionsOrLearningRate? ​

`number` | `AdaptiveWeightUpdateOptions`

Either a legacy numeric learning rate, or an options object with `namespace` and/or `learningRate`.

## Returns ​

`void`