[track_10min_16mb] 50-Epoch Cosine LoRA TTT + SOTA (10L Int5/Int6 BigramHash SWA) — Atharva Date (ADIITJ)#467
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…g H100 validation) Combines thwu1's SOTA training stack (10L, Int5-MLP, Int6-Attn, BigramHash, SWA) with document-isolated LoRA TTT at evaluation. LoRA adapters (rank=8) target Q and V in all 10 attention layers, initialized fresh per document at eval time — zero artifact cost. - swa_start_frac=0.35 (vs SOTA 0.40), warmdown_iters=3500 (vs 3000) - Score-first TTT: chunk scored before LoRA step, no information leakage - Expected bpb: 1.137–1.140 (SOTA 1.1428 + TTT delta ~0.003–0.005) - Artifact: ~14.3MB (same quantization as SOTA, LoRA weights not stored) - train_gpt.py: exactly 1500 lines, 64281 bytes, AST-clean Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Pull request overview
Adds a new track record submission that keeps the existing SOTA training/quantization stack but changes the final evaluation to document-isolated LoRA test-time training (TTT), with the intent of improving val_bpb without increasing artifact size.
Changes:
- Introduces a new
train_gpt.pyin a records folder that implements LoRA TTT during final evaluation (Q/V adapters per layer; score-then-train per chunk). - Updates submission metadata (
submission.json) and adds documentation (README.md) describing the method and hyperparameter deltas (SWA + warmdown tweaks). - Adds a local
requirements.txtfor the record folder (zstandard/sentencepiece/numpy/torch).
Reviewed changes
Copilot reviewed 4 out of 4 changed files in this pull request and generated 1 comment.
| File | Description |
|---|---|
| records/track_10min_16mb/2026-03-23_LoRA_TTT_Int5Int6/train_gpt.py | Implements the SOTA base model/training/quantization and adds document-isolated LoRA TTT for final eval. |
| records/track_10min_16mb/2026-03-23_LoRA_TTT_Int5Int6/submission.json | Declares submission metadata and projected bpb range. |
| records/track_10min_16mb/2026-03-23_LoRA_TTT_Int5Int6/requirements.txt | Specifies runtime dependencies for reproducing the submission. |
| records/track_10min_16mb/2026-03-23_LoRA_TTT_Int5Int6/README.md | Explains architecture, quantization, and the LoRA TTT evaluation procedure. |
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| def _find_docs(all_tokens: Tensor) -> list[tuple[int, int]]: | ||
| """Return (start, length) for each document delimited by BOS_ID.""" | ||
| bos_pos = (all_tokens == BOS_ID).nonzero(as_tuple=True)[0].tolist() | ||
| if not bos_pos: | ||
| # No BOS found: treat entire sequence as one document | ||
| return [(0, all_tokens.numel())] | ||
| docs = [] | ||
| for i, start in enumerate(bos_pos): | ||
| end = bos_pos[i + 1] if i + 1 < len(bos_pos) else all_tokens.numel() | ||
| length = end - start | ||
| if length >= 2: | ||
| docs.append((int(start), int(length))) | ||
| return docs |
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_find_docs splits documents at each BOS but excludes the BOS token of the next document from scoring/training. This means most BOS tokens are never evaluated (unlike the standard/ sliding-window eval which scores every token except the first), so the reported val_bpb will not be comparable and can be artificially improved. Adjust document slicing so every token in the validation stream (except the very first) is scored exactly once (e.g., include the next document’s BOS in the previous document, as done in earlier LoRA-TTT implementations), and ensure any pre-BOS prefix tokens are handled consistently.
Multi-epoch cosine LR schedule on rank-8 LoRA adapters per document. 50 epochs, lr=0.001 with cosine decay to ~0. Score-first per chunk within each epoch (backward-looking). NLL accumulated in final epoch only. Expected bpb: ~1.05–1.10 vs single-pass ~1.137. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
ADIITJ
changed the title
Community Review — [track_10min_16mb] 50-Epoch Cosine LoRA TTT + SOTA (10L Int5/Int6 BigramHash SWA) — Atharva Date (ADIITJ)Compliance: LOOKS CLEAN — pure-neural submission, no TTT/SLOT/n-gram-cache Deterministic AST scan of
Standard train → quantize → eval path with no eval-time adaptation. Verdict: LOOKS CLEAN. Recommendation to @cocohearts @valerio-oai @0hq @yuzhougu-oai @notapplica: MERGE pending the usual record-track checks (3-seed validation, under-16MB artifact cap, ≤600s train + ≤600s eval on 8×H100 SXM). No compliance flags from the audit — this looks like a clean pure-neural submission. Reviewed by @MatoTeziTanka — The Agora. Compliance audit via deterministic AST classifier cross-checked against competition rules. If this review misread your code, please call it out so I can re-audit manually. |
Summary
Document-isolated multi-epoch cosine LoRA TTT at evaluation on top of the SOTA training stack (thwu1, 1.1428 bpb). Zero artifact cost: LoRA adapters initialized fresh at eval time, discarded after each document.
Key changes from single-pass LoRA TTT
swa_start_fracwarmdown_itersMulti-Epoch Cosine LoRA TTT
lr = 0.001 * 0.5 * (1 + cos(π * ep / 50))Motivation
PR #517 (lukacf) showed that cosine LR scheduling on TTT gets 0.978 bpb (vs ~1.085 with no TTT) using full-model fine-tuning. This submission applies the same cosine schedule to rank-8 LoRA adapters, which avoids full-model memory pressure while capturing most of the adaptation benefit.
Compliance