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Checkpoints and the numbers they reproduce

All files are PyTorch weights of ELF-B (105M). Evaluate with scripts/evaluate.sh TASK FILE (deployment sampler gamma56@64, gamma28@32 on Sudoku; classifier-free guidance 2). Accuracies are on the test half of the held-out prompts (odd indices), the protocol of Table 1.

Two numbers are given for every file. Release eval is what scripts/evaluate.sh prints: the mean over the 5 default sampler seeds, with the standard deviation across seeds. Paper is the single evaluation of the same file in the research harness that went into the paper; it sits within the seed spread of the release eval.

Base models: checkpoints/base/<task>.pt

Task-SFT ELF-B, the starting point of every RL run.

task release eval (5 seeds) paper
countdown 41.8 ± 0.8 42.8
sudoku 63.1 ± 0.7 64.0
kk 67.6 ± 0.3 68.2
game24 44.5 ± 1.3 44.0

Critics: checkpoints/critic/<task>_{1d,2d}.pt

Pre-trained critics (denoiser copy + head) that the SVG runs start from; the mode field inside the file selects CFRL-SVG-1D or CFRL-SVG-2D in train_svg.py.

RL checkpoints: checkpoints/{grr,svg1d,svg2d}/<task>.pt

One checkpoint per task and variant: the best remaining checkpoint of the runs behind Table 1. Table 1 reports the mean over three runs; the shipped file is one run, chosen as the best of them, so its paper number sits at or above that mean.

task variant file release eval (5 seeds) paper (this file) Table 1 (mean of 3 runs)
countdown CFRL-GRR grr/countdown.pt 41.7 ± 1.0 43.0 42.6
countdown CFRL-SVG-1D svg1d/countdown.pt 46.6 ± 1.0 48.4 47.4
countdown CFRL-SVG-2D svg2d/countdown.pt 49.4 ± 0.7 50.5 49.7
sudoku CFRL-GRR grr/sudoku.pt 69.9 ± 1.2 71.2 67.9
sudoku CFRL-SVG-1D svg1d/sudoku.pt 66.3 ± 0.6 65.2 64.1
sudoku CFRL-SVG-2D svg2d/sudoku.pt 69.0 ± 0.5 69.1 68.7
kk CFRL-GRR grr/kk.pt 74.5 ± 0.5 73.9 71.4
kk CFRL-SVG-1D svg1d/kk.pt 78.1 ± 0.8 77.5 76.5
kk CFRL-SVG-2D svg2d/kk.pt 79.3 ± 0.6 79.8 77.5
game24 CFRL-GRR grr/game24.pt 49.0 ± 0.5 48.8 47.4
game24 CFRL-SVG-1D svg1d/game24.pt 53.3 ± 0.6 52.9 51.2
game24 CFRL-SVG-2D svg2d/game24.pt 58.6 ± 0.9 59.6 58.2

Training iterations of the shipped RL checkpoints (GRR / SVG-1D / SVG-2D): countdown 500 / 750 / 1500; sudoku 500 / 250 / 500; kk 600 / 1000 / 1000; game24 600 / 1250 / 1500.