Discrete Diffusion VLA: Bringing Discrete Diffusion to Action Decoding
  in Vision-Language-Action Policies
Vision-Language-Action (VLA) models adapt large vision-language backbones to
map images and instructions to robot actions. However, prevailing VLA decoders
either generate actions autoregressively in a fixed left-to-right order or
attach continuous diffusion or flow matching heads outside the backbone,
demanding specialized training and iterative sampling that hinder a unified,
scalable architecture. We present Discrete Diffusion VLA, a single-transformer
policy that models discretized action chunks with discrete diffusion and is
trained with the same cross-entropy objective as the VLM backbone. The design
retains diffusion's progressive refinement paradigm while remaining natively
compatible with the discrete token interface of VLMs. Our method achieves an
adaptive decoding order that resolves easy action elements before harder ones
and uses secondary remasking to revisit uncertain predictions across refinement
rounds, which improves consistency and enables robust error correction. This
unified decoder preserves pretrained vision language priors, supports parallel
decoding, breaks the autoregressive bottleneck, and reduces the number of
function evaluations. Discrete Diffusion VLA achieves 96.3% avg. SR on LIBERO,
71.2% visual matching on SimplerEnv Fractal and 49.3% overall on SimplerEnv
Bridge, improving over both autoregressive and continuous diffusion baselines.
These findings indicate that discrete-diffusion action decoder supports precise
action modeling and consistent training, laying groundwork for scaling VLA to
larger models and datasets.
68afcafd245176306494cc2d
Zhixuan Liang
admin_assigned
2025-08-28T19:01:23.534Z
662a471e94baa018b00c0f5c
/avatars/62a67a2ee6e4b9a7124f8b02b9b3f280.svg
Zhixuan Liang
user
Liang-ZX
68afcafd245176306494cc2e
Yizhuo Li
admin_assigned
2025-08-28T19:01:32.296Z
630b094f8b327c7b8b94d24c
https://cdn-avatars.huggingface.co/v1/production/uploads/630b094f8b327c7b8b94d24c/Fd0ugLOHJZIi8oUZScOmX.jpeg
Yizhuo Li
user
liyz
68afcafd245176306494cc2f
Tianshuo Yang
admin_assigned
2025-08-28T19:01:39.348Z
675875d45f2b9e156b83c57a
/avatars/53f3e400db2692231b4db7eaf23b0575.svg
Tianshuo Yang
user
Soultfz
68afcafd245176306494cc30
Chengyue Wu
admin_assigned
2025-08-28T19:01:45.408Z
68ae7557e1effd4a43b675b8
/avatars/870f91c8834bea68e3af002c61c01560.svg
Chengyue Wu
user
chengyuew
68afcafd245176306494cc31
Sitong Mao
68afcafd245176306494cc32
Liuao Pei
68afcafd245176306494cc33
Xiaokang Yang
admin_assigned
2025-08-28T19:01:56.672Z
64eaba6b1d8b651a9aed1203
/avatars/a0f26095c262ca1a204c27ff4bfb188c.svg
yangxiaokang
user
zicheqingluo
68afcafd245176306494cc34
Jiangmiao Pang
68afcafd245176306494cc35
Yao Mu
68afcafd245176306494cc36
Ping Luo
Discrete Diffusion VLA: Bringing Discrete Diffusion to Action Decoding
  in Vision-Language-Action Policies
Vision-Language-Action (VLA) models adapt large vision-language backbones to
map images and instructions to robot actions. However, prevailing VLA decoders
either generate actions autoregressively in a fixed left-to-right order or
attach continuous diffusion or flow matching heads outside the backbone,
demanding specialized training and iterative sampling that hinder a unified,
scalable architecture. We present Discrete Diffusion VLA, a single-transformer
policy that models discretized action chunks with discrete diffusion and is
trained with the same cross-entropy objective as the VLM backbone. The design
retains diffusion's progressive refinement paradigm while remaining natively
compatible with the discrete token interface of VLMs. Our method achieves an
adaptive decoding order that resolves easy action elements before harder ones
and uses secondary remasking to revisit uncertain predictions across refinement
rounds, which improves consistency and enables robust error correction. This
unified decoder preserves pretrained vision language priors, supports parallel
decoding, breaks the autoregressive bottleneck, and reduces the number of
function evaluations. Discrete Diffusion VLA achieves 96.3% avg. SR on LIBERO,
71.2% visual matching on SimplerEnv Fractal and 49.3% overall on SimplerEnv
Bridge, improving over both autoregressive and continuous diffusion baselines.
These findings indicate that discrete-diffusion action decoder supports precise
action modeling and consistent training, laying groundwork for scaling VLA to
larger models and datasets.
Discrete Diffusion VLA uses a single-transformer policy with discrete diffusion to model actions, improving decoding order, consistency, and performance over autoregressive and continuous diffusion methods.
vision-language-action models VLA decoders autoregressive continuous diffusion flow matching single-transformer policy discrete diffusion cross-entropy objective discrete token interface adaptive decoding order secondary remasking pretrained vision language priors parallel decoding autoregressive bottleneck function evaluations LIBERO SimplerEnv Fractal SimplerEnv Bridge