We lifted 8 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| ozekri/SEPO | canonical | 1 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| fsspec_exists | Ran | ozekri/SEPO/GRPO_MDLM_DNA/utils.py code served (permissive licence) · get_code("dec1de00023a923b") |
| batch_dna_detokenize | Not yet run | ozekri/SEPO/GRPO_MDLM_DNA/dataloader_gosai.py code served (permissive licence) · get_code("4ec57ee85c940684") |
| binary_discretization | Not yet run | ozekri/SEPO/GRPO_MDLM_DNA/noise_schedule.py code served (permissive licence) · get_code("9b9814cca50339d7") |
| dna_detokenize | Not yet run | ozekri/SEPO/GRPO_MDLM_DNA/dataloader_gosai.py code served (permissive licence) · get_code("b988ba95b058558a") |
| dna_tokenize | Not yet run | ozekri/SEPO/GRPO_MDLM_DNA/dataloader_gosai.py code served (permissive licence) · get_code("733f359f0f876fb7") |
| fsspec_listdir | Not yet run | ozekri/SEPO/GRPO_MDLM_DNA/utils.py code served (permissive licence) · get_code("db0136eb80a50383") |
| get_logger | Not yet run | ozekri/SEPO/GRPO_MDLM_DNA/utils.py code served (permissive licence) · get_code("60dbd956810fafb6") |
| get_noise | Not yet run | ozekri/SEPO/GRPO_MDLM_DNA/noise_schedule.py code served (permissive licence) · get_code("fe6314378d2def00") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Discrete diffusion models have recently gained significant attention due to their ability to process complex discrete structures for language modeling. However, fine-tuning these models with policy gradient methods, as is commonly done in Reinforcement Learning from Human Feedback (RLHF), remains a challenging task. We propose an efficient, broadly applicable, and theoretically justified policy gradient algorithm, called Score Entropy Policy Optimization (\SEPO), for fine-tuning discrete diffusion models over non-differentiable rewards. Our numerical experiments across several discrete generative tasks demonstrate the scalability and efficiency of our method. Our code is available at https://github.com/ozekri/SEPO.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2502.01384")
get_code_for_paper("2502.01384")
have("2502.01384")
Connect an agent — have() is free.