Three related bugs from the 2c.3a GRN trunk reshuffle that the
bottleneck-Linear runtime sites silently inherited:
1. **on_w_ptrs[24/25] -> [33/34]** (4 sites in gpu_dqn_trainer.rs).
2c.3a's +9 shift migrated branch/value/VSN/GLU/KAN consumers but
missed the bottleneck Linear's forward GEMM (line ~14098), forward
bias-add (~14107), and backward dW/db offsets (~15109-15110).
Post-2c.3a, indices 24/25 point at b_b1out (153 floats) and start
of w_b2fc (4288 floats) — not w_bn (672 floats) / b_bn (16 floats).
Forward + backward were self-consistent on wrong tensors; the actual
w_bn/b_bn at documented indices 33/34 sat untouched as zeros for
~10 commits. Smoke kept passing because GRN's Linear_residual
projection ran in parallel and provided a clean residual-only
pathway around the corrupted bottleneck slot.
2. **Target net missing bottleneck path.** forward_target_raw passed
raw next_states_buf (128-padded) to an encoder expecting
[B, s1_input_dim=102]; it was reading
[market|ofi|tlob|mtf] of next_states instead of
[bn_market_proj|portfolio]. Fix: build tg_bn_concat_buf inline
before forward_target_raw using tg_w_ptrs[33]/[34] (target's w_bn)
on next_states_buf[market]; new buffer pair tg_bn_hidden_buf +
tg_bn_concat_buf.
3. **DDQN argmax missing bottleneck path.** submit_forward_ops_ddqn
had the same shape mismatch on the online-on-next_states pass.
Fix: build on_next_bn_concat_buf using on_w_ptrs[33]/[34] (online
weights, since DDQN argmax uses online net); new buffer pair
on_next_bn_hidden_buf + on_next_bn_concat_buf.
Three call sites of the existing bn_tanh_concat_kernel now: online-on-
states (states + on_w_bn), target-on-next_states (next + tg_w_bn), and
ddqn-online-on-next_states (next + on_w_bn). Each combination of
weights × input states produces distinct features; sharing the kernel
across distinct buffer pairs preserves the GPU-only cold-path contract.
No fingerprint change (no ISV slot or param tensor added).
Smoke validation (multi_fold_convergence, 700.43s, 3 folds x 5 epochs):
fold 0 best Sharpe 6.53 at epoch 2
fold 1 best Sharpe 80.11 at epoch 4
fold 2 best Sharpe 66.72 at epoch 4
geom-mean: 39.27 (vs Task 3 20.03, 2c.3c.6 18.80)
The Sharpe lift is consistent with the bottleneck Linear now seeing
its actual weights and the target/DDQN nets seeing input-shape-
consistent features for TD-target / argmax-action computations.
+166 / -9 LOC all in gpu_dqn_trainer.rs.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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