2026-03-27_PhaseCoherenceGatedGradients submission

.gitignore

@@ -8,4 +8,4 @@ data/manifest.json
 data/docs_selected.jsonl
 .mypy_cache/
 .venv
-logs/
+logs/

data/README.md

@@ -63,4 +63,4 @@ MATCHED_FINEWEB_TIKTOKEN_THREADS=16
 MATCHED_FINEWEB_GPT2_DECODE_BATCH_SIZE=512
 ```
 
-These control batched tokenizer encoding during shard export, tokenizer thread count, tiktoken thread count, and batched GPT-2 decode for the blobstore docs-cache path.
+These control batched tokenizer encoding during shard export, tokenizer thread count, tiktoken thread count, and batched GPT-2 decode for the blobstore docs-cache path.

records/track_non_record_16mb/2026-03-27_PhaseCoherenceGatedGradients/README.md

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+# Phase Coherence Gated Gradients
+
+Exploratory PIC-GD experiment folder for `2026-03-27`. This is not a leaderboard submission package yet.
+
+The script in this folder adapts the baseline training loop to a batch-level version of phase-induced coherence-gated gradient descent (PIC-GD) while keeping:
+
+- the real-valued transformer architecture
+- the Muon + Adam optimizer split
+- tokenizer-agnostic `val_bpb` evaluation
+- the int8 + zlib roundtrip export path
+
+## Current Status
+
+This folder should be treated as an experiment source of truth, not as a finished submission.
+
+- current reported result: single exploratory `8x H100` run around `1.3178 val_bpb`
+- not competitive with the current `track_10min_16mb` leaderboard
+- `submission.json` remains intentionally unbenchmarked
+- generated artifacts like `final_model.pt` and `final_model.int8.ptz` should not be part of the eventual PR
+
+## PIC-GD Adaptation
+
+The implementation stays close to the baseline training loop:
+
+- final hidden states are treated as pseudo-complex latents by pairing adjacent channels as `(real, imag)`
+- target-token embeddings are paired the same way to provide a reference signal
+- a normalized coherence score is computed from the paired latent/reference dot product
+- the coherence score is converted into a detached gradient gate
+
+```python
+alpha = PICGD_MIN_GATE + (1 - PICGD_MIN_GATE) * sigmoid(PICGD_BETA * coherence)
+```
+
+Training backpropagates `loss * alpha`, while validation and final quantized roundtrip evaluation continue to use raw cross-entropy only.
+
+## Current Experimental Defaults
+
+- `PICGD_ENABLED=1`
+- `PICGD_BETA=2.0`
+- `PICGD_MIN_GATE=0.05`
+- `PICGD_EPS=1e-6`
+- `PICGD_TOKEN_STRIDE=32`
+- `attention_impl` is logged as `native_gqa`, `kv_repeat_fallback`, or `standard_sdpa`
+
+Training logs include:
+
+- `picgd_coherence`
+- `picgd_gate`
+- `attention_impl`
+
+## Evidence Standard Before Packaging
+
+Do not rewrite this folder as a real submission until the following exists on `8x H100`:
+
+- 1 baseline run with root `train_gpt.py`
+- 3 PIC-GD runs with this folder's `train_gpt.py`
+- recorded seeds, `step_avg`, final quantized `val_bpb`, artifact size, and peak memory for every run
+- a positive baseline-vs-PIC-GD comparison that justifies keeping the method
+
+If PIC-GD does not beat the baseline mean on the same setup, stop pursuing it for submission.