All RL head gradients flowing into the shared encoder (π, V, FRD,
outcome) are now scaled by 1/B at the grad_h_accumulate point.
Without this, doubling batch size doubles the encoder gradient from
each head, creating batch-size-dependent training dynamics.
Per-head Adam optimizers are self-normalizing (m/sqrt(v)), so their
weight gradients don't need explicit /B. Only the shared encoder
accumulation point matters.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
8dac9f5f00