This checkpoint is part of the dLLM Registers project — register tokens as a bounded, trained, continuous channel for carrying decoding state across denoising windows in diffusion language models.

Paper section: slot-count scaling figure (N=8 registers)
Carry channel: continuous register tokens (channel_mode=registers, num_registers=8, tail_length=0)
Base model: GSAI-ML/LLaDA-8B-Base
Training data: OpenMathInstruct-2 subset of mix60k (~30K math traces, oci_python excluded)
Training config: SFT/amlt/chunked_sft_omi30k_scaling_round2_bonete54.yaml
Chunk size: C = 128 tokens
Prompt dropout rate (Bernoulli per-trace CSG mask): 0.3
Date uploaded: 2026-06-13

How to load

python

from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("albertge/llada-8b-dllm-registers-omi30k-r8", trust_remote_code=True)
tok = AutoTokenizer.from_pretrained("albertge/llada-8b-dllm-registers-omi30k-r8", trust_remote_code=True)

To use the carry channel correctly at inference, see the evaluator at eval/eval.py and the wrapper eval/run_mix60k_full_eval.sh. Key flags for this checkpoint: --num_registers 8 --channel_mode registers --tail_length 0 --use_mask_token_for_registers

Repository

Training and eval code: https://github.com/lbertge/d1-registers

Citation

If you use this checkpoint, please cite the dLLM Registers paper:

bibtex

@misc{dllm-registers-2026,
  title  = {Register Tokens for Unbounded Reasoning in Diffusion Language Models},
  author = {Albert Ge and collaborators},
  year   = {2026},
  note   = {Preprint},
  url    = {https://github.com/lbertge/d1-registers}
}