moe/TRAINING_PLAN.md

# MoE Training Plan

## Data Mix

Train only on manifest-verified JSONL checkpoints. Do not point the trainer at
raw source directories.

Primary input:

```text
dataset/<checkpoint>/dataset/*.jsonl
```

Each 20 GiB checkpoint should be treated as an atomic training unit. Keep the
checkpoint report beside the run metadata so loss curves can be traced back to
the exact JSONL files.

## Curriculum

1. Warm up on short and medium FIM examples.
2. Add long repository-context examples.
3. Add code generation as an auxiliary continuation task.
4. Fine-tune with real-code FIM weighted higher than synthetic continuation.

## H100 Defaults

- Context: start at 2048, then ablate 4096.
- Precision: bf16 on H100.
- Optimizer: AdamW.
- Grad clipping: enabled.
- MoE aux loss: start small and tune from expert load.
- Top-k: top-2 for training, top-1 as inference ablation only.
- Capacity factor: 1.25 for training.
- Router z-loss: enabled from step 0.
- Router jitter: small nonzero value during early training.
- MTP loss: optional, low coefficient, disabled if next-token validation
  worsens.

## Preprocessing

- Preserve whitespace and FIM markers exactly.
- Append `<|endoftext|>` between packed records.
- Bucket by token length before batching.
- Track domain and language metadata for router analysis.
- Do not rebalance by duplicating JSONL files. Rebalance in the dataloader or
  sampler to avoid SSD growth.

## Router Guardrails

- If one expert holds more than 35% sustained load, raise aux loss or increase
  shared capacity.
- If dropped tokens exceed 1%, reduce batch tokens or increase capacity.
- If router entropy collapses early, lower LR or increase warmup.
- If domain-to-expert correlation is too strong, improve data mix before adding
  experts.