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README.md

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+---
+license: mit
+library_name: pytorch
+tags:
+  - depth-recurrent
+  - weight-tied
+  - looped-transformer
+---
+
+# Depth-Recurrent Transformer (S1, step 49209)
+
+Final recursive checkpoint for the paper *Per-Token Fixed-Point Convergence in
+Depth-Recurrent Transformers, and Why Reading It Beats Learning It*.
+
+A depth-recurrent (weight-tied, looped) transformer: prelude blocks, a
+weight-tied core looped `r` times with the recursion count sampled per
+optimization step during pretraining, then coda blocks. Trained on FineWeb-Edu
+for 12.9B tokens.
+
+- Parameters: 85.6M (state dict); ~57.3M active per forward at r=1
+- Training tokens: 12,900,106,240
+- Step: 49209
+
+## Contents
+
+This is an inference checkpoint (`config` + `state_dict` only; optimizer and RNG
+state removed). It is a `torch.save` dict and requires the model code from the
+[code release](https://github.com/jlognn/depth-recurrent-convergence) to load.
+
+```python
+import torch
+from src.model import build_model
+
+ck = torch.load("model.pt", map_location="cpu", weights_only=False)
+model = build_model(ck["config"])
+model.load_state_dict(ck["state_dict"])
+model.eval()
+
+# recursive forward takes a loops= kwarg
+logits = model(input_ids, loops=8)
+```
+
+Or evaluate directly with the release harness:
+
+```bash
+python bench/benchmark.py --ckpt model.pt --tokens data/fwe-val.bin --loops 1 2 4 8 16 32
+python bench/allocate.py  --ckpt model.pt --tokens data/fwe-val.bin --diagnostic
+```
+
+## Citation
+
+```
+@misc{logan2026pertoken,
+  title  = {Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers,
+            and Why Reading It Beats Learning It},
+  author = {Logan, Joe},
+  year   = {2026},
+  eprint = {arXiv:XXXX.XXXXX}
+}
+```

model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:571b8ed34c7ab9671985586158c6c13fc027eba542095dc41177ef5d1f9e2265
+size 229230136