CFRL checkpoints: RL post-training of the ELF-B continuous flow language model

Checkpoints for the paper CFRL (reinforcement learning for continuous flow language models). Every file is a PyTorch weights file of ELF-B (105M parameters), the embedded-language-flow model of ELF, fine-tuned on four program-verified reasoning tasks (Countdown, Game24, Sudoku, Knights-and-Knaves) and then post-trained with one of three RL variants.

The code that trains, evaluates and loads these files is the cfrl-release repository (cfrl/common.py:load_weights, scripts/evaluate.sh). This repository holds only the weights.

Layout

folder	files	what
base/	<task>.pt	task-SFT ELF-B, the starting point of every RL run
critic/	<task>_1d.pt, <task>_2d.pt	critics (denoiser copy + MLP head) used by CFRL-SVG-1D / CFRL-SVG-2D
grr/	<task>.pt	CFRL-GRR policy, one per task
svg1d/	<task>.pt	CFRL-SVG-1D policy, one per task
svg2d/	<task>.pt	CFRL-SVG-2D policy, one per task

<task> is one of countdown, game24, sudoku, kk. Model files are dicts with params, ema_params1 and step (EMA weights are used when present); critic files hold trunk, head and mode.

Results

Test accuracy, Table 1.

task	base	CFRL-GRR	CFRL-SVG-1D	CFRL-SVG-2D
Countdown	42.8	42.6	47.4	49.7
Sudoku	64.0	67.9	64.1	68.7
Knights-and-Knaves	68.2	71.4	76.5	77.5
Game24	44.0	47.4	51.2	58.2

Usage

# in the cfrl-release repository, with its environment
hf download tufts-ml/CFRL --local-dir checkpoints
scripts/evaluate.sh countdown checkpoints/svg2d/countdown.pt
scripts/train_svg.sh countdown 2d        # CFRL-SVG-2D from base/countdown.pt with critic/countdown_2d.pt

The files are plain torch.save dicts and load with torch.load(path, map_location="cpu", weights_only=False).

License

MIT, as the code release. ELF-B itself is released under the MIT license of its authors.

Citation

@article{cfrl2026,
  title   = {CFRL: Reinforcement Learning for Continuous Flow Language Models},
  year    = {2026}
}