pith:SSGN4R6L
Fast-WAM: Do World Action Models Need Test-time Future Imagination?
World Action Models achieve competitive performance without generating future observations at test time.
arxiv:2603.16666 v2 · 2026-03-17 · cs.CV · cs.AI
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Claims
Fast-WAM remains competitive with imagine-then-execute variants, while removing video co-training causes a much larger performance drop. It achieves competitive results with state-of-the-art methods on simulation benchmarks (LIBERO and RoboTwin) and real-world tasks, without embodied pretraining, running in real time with 190ms latency.
That the proposed Fast-WAM variants successfully disentangle the contribution of video modeling during training from explicit future generation at inference, allowing a controlled comparison of the two factors.
Fast-WAM shows that explicit future imagination at test time is not required for strong WAM performance; video modeling during training provides the main benefit.
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| First computed | 2026-05-18T02:45:49.533935Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
948cde47cb429582af339a5fa40a2560e2e14364cd856a55a1aa17f870c450df
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/SSGN4R6LIKKYFLZTTJP2ICRFMD \
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Canonical record JSON
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