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VFlash cuts per-step latency 5.1 to 5.2x on Jetson Thor and DGX Spark, and its one-pass drafter beats the autoregressive 4B model it drafts for on 3D robot trajectories.
A planner does not have to write points, boxes and trajectories one coordinate token at a time. A primitive's type, dimensionality and length fix its layout before decoding, so a lightweight drafter fills the whole primitive in one forward pass. The decoder accepts it if it is structurally valid, with no token agreement with the target. Text keeps verified speculative decoding.
🔍 Why token matching fails
• DFlash commits 5.5 tokens per iteration on subtask text but only 2.2 inside trajectory blocks, so it only about halves decoding time
• A coordinate one bin off fails the check even when the geometric error is small
📊 The numbers
• AgiBot waypoints within 5 cm: 50% to 73% (3D only) and 40% to 74% (2D+3D); ADE falls 18 to 61% across InternA1, AgiBot and MolmoBot
• Under AR, decoding is 75 to 87% of a step on Thor; VFlash cuts it 12 to 42x. H100 gets 3.6x
• MolmoSpaces success: 70.2% with VFlash plans, 62.6% baseline, 61.0% with AR plans
⚠️ Caveats
• Latency is a pipeline measurement at batch size one, with a 4B target and 36 to 84 token primitives
• Boxes need an AR fallback: without it, mean F1 drops about 20 points on Dense200
• The 25x dense point motion figure is preliminary, and accuracy there is mixed
VFlash: Fast, Accurate Visual Planning via Flash Primitive Decoding (2611.08449)
https://arxiv.org/abs/2611.08449
If your planner's cost is mostly coordinate decoding, would you swap token-level verification for a structure validator?
📚 Read it elsewhere
• The deep dive on Medium: https://medium.com/p/26ed0ca0bb84
• The long read on Substack: https://sajjadpakdamansavoji.substack.com/p/vflash-drafts-whole-robot-plans-in
• The thread on X: https://x.com/saavoji/status/2118773042503623062
• Every version on sajjadpsavoji.github.io: https://sajjadpsavoji.github.io/blog#vflash-drafts
A planner does not have to write points, boxes and trajectories one coordinate token at a time. A primitive's type, dimensionality and length fix its layout before decoding, so a lightweight drafter fills the whole primitive in one forward pass. The decoder accepts it if it is structurally valid, with no token agreement with the target. Text keeps verified speculative decoding.
🔍 Why token matching fails
• DFlash commits 5.5 tokens per iteration on subtask text but only 2.2 inside trajectory blocks, so it only about halves decoding time
• A coordinate one bin off fails the check even when the geometric error is small
📊 The numbers
• AgiBot waypoints within 5 cm: 50% to 73% (3D only) and 40% to 74% (2D+3D); ADE falls 18 to 61% across InternA1, AgiBot and MolmoBot
• Under AR, decoding is 75 to 87% of a step on Thor; VFlash cuts it 12 to 42x. H100 gets 3.6x
• MolmoSpaces success: 70.2% with VFlash plans, 62.6% baseline, 61.0% with AR plans
⚠️ Caveats
• Latency is a pipeline measurement at batch size one, with a 4B target and 36 to 84 token primitives
• Boxes need an AR fallback: without it, mean F1 drops about 20 points on Dense200
• The 25x dense point motion figure is preliminary, and accuracy there is mixed
VFlash: Fast, Accurate Visual Planning via Flash Primitive Decoding (2611.08449)
https://arxiv.org/abs/2611.08449
If your planner's cost is mostly coordinate decoding, would you swap token-level verification for a structure validator?
📚 Read it elsewhere
• The deep dive on Medium: https://medium.com/p/26ed0ca0bb84
• The long read on Substack: https://sajjadpakdamansavoji.substack.com/p/vflash-drafts-whole-robot-plans-in
• The thread on X: https://x.com/saavoji/status/2118773042503623062
• Every version on sajjadpsavoji.github.io: https://sajjadpsavoji.github.io/blog#vflash-drafts