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README.md

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+---
+license: mit
+tags:
+- text-generation
+- from-scratch
+- grpo
+- reinforcement-learning
+- arithmetic-reasoning
+---
+
+# tinyzero-countdown-19m
+
+A ~18.9M-parameter decoder-only transformer, pretrained from scratch and
+post-trained with GRPO (Group Relative Policy Optimization) to solve
+Countdown-style arithmetic puzzles: given a set of numbers and a target,
+find an equation using each number exactly once that reaches the target.
+
+## Architecture
+RoPE positional embeddings, RMSNorm, grouped-query attention (via
+`F.scaled_dot_product_attention`), SwiGLU MLP, tied embeddings. Custom
+8192-token BPE vocabulary trained on the pretraining corpus (not GPT-2's
+tokenizer -- see rationale below).
+
+- Parameters: ~18.88M (verified exactly, not estimated)
+- Context length: 256
+- Vocab size: 8192 (custom-trained BPE)
+- d_model: 384, layers: 10, heads: 6 (2 KV heads, GQA)
+
+## Training pipeline and what I learned building it
+
+**Pretraining**: ~380M tokens on FineWeb-Edu + synthetic arithmetic
+text, at a ~20:1 token:parameter ratio (Chinchilla-optimal). An earlier
+attempt at 116M params / 150M tokens (1.3:1 ratio) showed the failure mode
+directly: healthy train/val loss gap but weak generalization. This version
+also fixes a subtler issue -- at small model scale, a standard 50k-token
+vocabulary's embedding table dominates the parameter budget (60-75% of
+total params); training a small custom vocab instead keeps embedding
+overhead to ~17%, leaving actual capacity for reasoning.
+
+**SFT**: an instruction-format fine-tune initially looked successful by
+loss (train 1.02->0.34) but generation accuracy was 0% -- a real
+loss/accuracy divergence caused by a response template that was mostly
+easy-to-predict boilerplate, diluting the loss signal on the tokens that
+actually mattered (the numbers/operators). Root-caused to a large,
+un-bridged distribution shift between the pretraining corpus's format and
+the instruction phrasing; fixed by skipping the instruction wrapper and
+running GRPO directly on the pretrained checkpoint's native prompt format
+instead.
+
+**GRPO**: trained directly on the pretrained checkpoint, using the
+verifier (exact equation checker) as a binary+partial-credit reward, group-
+relative advantage normalization, PPO-style clipping, and a KL penalty
+against a frozen reference to prevent collapse. Result: 31.6% ->
+34.4% accuracy on a held-out 250-problem set (+2.8pp), with stable
+KL throughout (no collapse). This is a modest, honestly-reported effect --
+run at only ~500 steps on an 18.9M model, not a large or highly significant
+result, and reported with that caveat intentionally.
+
+## Usage
+
+```python
+import torch
+from tokenizers import ByteLevelBPETokenizer
+# adapt these imports to wherever you place model.py / config.py from this repo
+from model import TinyTransformer
+from config import ModelConfig
+
+mcfg = ModelConfig()
+model = TinyTransformer(mcfg)
+ckpt = torch.load("pytorch_model.pt", map_location="cpu")
+model.load_state_dict(ckpt["model_state_dict"])
+model.eval()
+
+tokenizer = ByteLevelBPETokenizer("vocab.json", "merges.txt")
+prompt = "Numbers: [12, 45, 7, 3], Target: 88, Equation:"
+ids = tokenizer.encode(prompt).ids
+x = torch.tensor([ids])
+out = model.generate(x, max_new_tokens=40, temperature=1.0, top_k=1)
+print(tokenizer.decode(out[0, len(ids):].tolist()))
+```
+
+## Limitations
+- Small model (~19M params) -- general text fluency is weak; this is
+  specialized for the countdown arithmetic task, not general-purpose use.
+- Only handles the raw prompt format shown above; natural-language
+  instruction phrasing was found to significantly degrade output quality
+  (see training notes above) and was not used for the released checkpoint.
+- Evaluated on synthetically generated countdown problems only.