Result: further epochs improved every domain — no forgetting
Validation perplexity fell from e2's 9.86 to 9.44. Measured against e2's
exact endpoint on the held-out forgetting probe, all three domains improved
rather than regressing — the transient rise while the fresh cosine LR peaked was
fully recovered and surpassed:
Table with columns: Domain, loss at e2 start, loss at e4 end, change| Domain | loss at e2 start | loss at e4 end | change |
|---|
| SEC filings | 1.7470 | 1.6977 | −2.82% |
| US case law | 2.2828 | 2.2324 | −2.21% |
| FineWeb-Edu | 3.1580 | 3.0987 | −1.88% |
So on this in-distribution corpus, a second round of continued pretraining behaves
as pure improvement, not catastrophic forgetting — the opposite of what the
single-epoch out-of-distribution extended run showed on SEC text.
Architecture
12 layers, 768 hidden, 12 heads, 1024 context, RoPE (θ=10000), RMSNorm, SwiGLU,
tied embeddings, vocab 16,384 (125.8M params). Same tokenizer and embedding matrix
as slm-125m-base, so token ids mean exactly what they did in v1 training.
Not instruction-tuned — this is a base model. Use Ace-2504/slm-125m-base for the
original, or Ace-2504/slm-125m-e2 for the 2-epoch checkpoint.