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remove type exp
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@@ -101,7 +101,7 @@ def update_policy(
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if sample_weights is not None:
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# Use per-sample loss for weighted training
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# Note: Policies supporting sample weighting must implement forward(batch, reduction="none")
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per_sample_loss, output_dict = policy.forward(batch, reduction="none") # type: ignore[call-arg]
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per_sample_loss, output_dict = policy.forward(batch, reduction="none")
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# Apply sample weights: L_weighted = Σ(w_i * l_i) / (Σw_i + ε)
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# Weights are already normalized to sum to batch_size
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