scatter_insert_f32 is a 1D scalar kernel (5 args: dst, src, cursor, cap,
batch_size). insert_batch called it with 6 args for state matrices, passing
state_dim as batch_size. CUDA silently dropped the 6th arg (actual batch_size).
Result: only 96 floats (1 state row) inserted per experience batch into PER.
The model was training on ~99.99% uninitialized GPU memory. This bug affected
ALL state features, not just OFI — market features and portfolio were also
garbage in PER-sampled training batches.
Fix: added scatter_insert_f32_rows kernel (2D-aware, 6 args: dst, src,
cursor, cap, state_dim, batch_size) matching the existing scatter_insert
(bf16) pattern. States and next_states now use the row-aware kernel.
Verified locally: OFI_DIAG shows non-zero values through the full chain
(state_gather → env_step → PER insert → PER sample → trainer states_buf).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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