Stage 5: RAIL — Execution Plan

### Phase 0: Foundation (Week 1) **Priority**: P0 | **Machine**: Mac1 (controller) **Parallel tracks**: None (setup) | Step | Task | Machine | Est. | |------|------|---------|------| | 0.1 | Create `model/dit.py` with MotionDiT architecture (Tiny: 4 blocks/128dim, Full: 8 blocks/256dim) | Mac1 | 4h | | 0.2 | Create `model/flow_matching.py` with OT-CFM (training + Euler/midpoint sampling + CFG) | Mac1 | 4h | | 0.3 | Create `training/flow_losses.py` (flow matching loss + existing structure regularizers) | Mac1 | 2h | | 0.4 | Modify `config.py` to add FlowMatchingConfig and DiTConfig blocks | Mac1 | 1h | | 0.5 | Modify `training/trainer.py` to support flow matching training loop | Mac1 | 2h | | 0.6 | Modify `inference/sampler.py` to add ODE solvers alongside DDIM | Mac1 | 2h | | 0.7 | Create `scripts/train_flow.py` entry point | Mac1 | 1h | | 0.8 | Unit tests for DiT forward pass and flow matching loss | Mac1 | 2h | **Review checkpoint (end of Week 1)**: DiT builds, flow matching loss computes, sampling produces shaped output. ### Phase 1: Flow Matching Training (Weeks 2-4) **Priority**: P0 | **Machine**: Cloud GPU (training), Mac4/Mac5 (prototyping) | Step | Task | Machine | Est. | |------|------|---------|------| | 1.1 | Prepare training data: validate existing phrase bundles, compute statistics | Mac1 | 2h | | 1.2 | Train MotionDiT-Full (8 blocks) on phrase data with flow matching — initial run (10 epochs) | Cloud GPU | 8h | | 1.3 | **WEEK 2 CHECKPOINT**: Evaluate convergence. If loss not decreasing → pivot to DDIM consistency distillation | Mac1 | 1h | | 1.4 | Full training run (100 epochs) with WandB logging | Cloud GPU | 24h | | 1.5 | Compare quality: Flow 1-step vs Flow 4-step vs Flow 10-step vs DDIM-50 baseline | Mac1 | 4h | | 1.6 | Run SanityChecker + MusalityScorer on flow matching outputs vs DDIM outputs | Mac1 | 2h | | 1.7 | Select best step count for quality/speed tradeoff | Mac1 | 1h |