ADR-129: RuvLTRA Training Pipeline — All Phases Executing

README.md

@@ -100,6 +100,14 @@ User Query → [SONA Engine] → Model Response → User Feedback
 | 8h | [**GraphMAE**](./crates/ruvector-gnn) | Graph Masked Autoencoder — self-supervised node representation learning with GAT encoder |
 | 8i | [**TurboQuant**](./crates/ruvllm) | 2-4 bit asymmetric KV-cache quantization — 6-8x memory reduction, <0.5% perplexity loss, H2O/PyramidKV eviction |
 
+**Continuous Training & Optimization** *(ADR-129)*
+| # | Capability | What It Does |
+|---|------------|--------------|
+| 8j | [**Nightly training**](./scripts/training/) | Automated nightly LoRA fine-tuning from brain learnings — models improve every day |
+| 8k | [**Release gates**](./scripts/training/release_gate.py) | 7 automated quality checks (code quality, routing accuracy, perplexity, speed, contamination) — prevents shipping regressions |
+| 8l | [**TurboQuant profiling**](./crates/ruvllm/src/quantize/turboquant_profile.rs) | Per-layer KV-cache bit-width optimization with `.turboquant.json` sidecar configs |
+| 8m | [**Training corpus**](./data/training/) | 230+ records from brain memories (pi.ruv.io) + architecture decisions + Claude routing examples |
+
 **Distributed Systems**
 | # | Capability | What It Does |
 |---|------------|--------------|

docs/adr/ADR-129-ruvltra-gcloud-training-turboquant.md

@@ -16,9 +16,9 @@ Accepted — Phase 1 (calibration) deployed and executing. Governance and releas
 | **Cloud Run Jobs** | **3 deployed** | `ruvltra-calibration`, `ruvltra-nightly-train`, `ruvltra-benchmark` (all L4 GPU) |
 | **Cloud Schedulers** | **2 enabled** | Nightly 03:00 UTC, Weekly benchmark Mon 06:00 UTC |
 | **Phase 1: Calibration** | **Complete** | All 4 models calibrated on L4 GPU. TQ profiles + benchmarks uploaded to HuggingFace. Results: 75.4 tok/s (small), 62.6 tok/s (medium), 67.1 tok/s (claude-code) |
-| **Phase 2: SFT** | **Ready** | Training corpus exported (230 records, 530K tokens), scripts ready |
-| **Phase 3: Benchmarks** | **Partial** | Release gate automation implemented and tested; inference benchmarks running |
-| **Phase 4: Publishing** | **Partial** | TurboQuant sidecar configs uploaded to all 4 HF models |
+| **Phase 2: SFT** | **Executing** | LoRA SFT running on L4 GPU (rank-16, 2 epochs, lr=2e-5). Corpus: 230 records, 530K tokens |
+| **Phase 3: Benchmarks** | **Executing** | Release gate automation tested. L4 GPU benchmark job running. Calibration benchmarks complete for all 4 models |
+| **Phase 4: Publishing** | **Complete** | TurboQuant sidecar configs + benchmark results uploaded to all 4 HF models. Model card READMEs updated with benchmark tables |
 | **Tooling** | **ruvllm-native** | Uses RuvltraQuantizer + TurboQuantProfile (Rust), gguf + llama-cpp-python (Python). No llama.cpp source compilation. |
 
 ## Context