Add dense architecture docs

dense/README.md

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+# Dense Architecture
+
+The Dense model is the control baseline for this dataset. It should use the
+same tokenizer, data order, validation split, context length, and optimizer
+family as MoE so architecture changes can be compared cleanly.
+
+## Recommended Structure
+
+- Decoder-only Transformer.
+- RMSNorm.
+- RoPE positional encoding.
+- Grouped-query attention for lower KV-cache cost.
+- SwiGLU feed-forward blocks.
+- Tied embedding/head only after validation.
+- Native support for FIM tokens.
+
+## Evaluation
+
+Track:
+
+- Next-token validation loss.
+- FIM middle exact match.
+- FIM edit distance.
+- Syntax parse rate for languages where parsers are available.
+- Completion latency and tokens/sec.
+
+If Dense does not learn a checkpoint cleanly, inspect data schema, tokenizer,
+and objective mix before scaling MoE.

dense/TRAINING_PLAN.md

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+# Dense Training Plan
+
+## Setup
+
+Use the same JSONL checkpoint bundle as MoE. Keep random seed, batch token
+target, learning-rate schedule, and validation split aligned.
+
+## Curriculum
+
+1. Start with short and medium records.
+2. Add long FIM examples.
+3. Add continuation/code generation records.
+4. Evaluate FIM and next-token loss separately.
+
+## Stop Conditions
+
+- Validation loss diverges twice after LR reduction.
+- FIM metrics regress while train loss improves.
+- Tokenizer round-trip or special-token audit fails.