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Animesh Garg· @animesh_garg · X·· 11 days agoSignalEditorial score85

FLUX 3 Action by @bfl_ai is super cool!

Summary

FLUX 3 Action is an open-source 7B parameter world action model that achieves first place on the RoboLab benchmark. It outperforms previous models by 6.1 percentage points with 56% fewer parameters and runs 3.95x faster. The model supports fine-tuning for specific robots and tasks and is integrated into LeRobot with deployment on NVIDIA Jetson.

Editorial context

FLUX 3 Action is a 7B parameter world action model that achieves state-of-the-art performance on the RoboLab benchmark, outperforming previous models with fewer parameters and faster inference. It integrates action prediction with video generation and supports fine-tuning for specific robots and tasks.

Evidence and limits

Published automatically after robotics and source-evidence checks; no manual editorial approval is recorded. Source assertions are not independently verified. Missing information remains not reported.

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Reported quantities Scroll across to read all columns.
MetricValue / unitBasis / contextEvidence
percent42.9 percentBasis not reported

Source wording: “42.9% vs. 36.8%”

Source E1
percent6.1 percentBasis not reported

Source wording: “6.1 percentage points”

Source E2
percent29 percentBasis not reported

Source wording: “29% and runtimes by 40%”

Source E3
percent40 percentBasis not reported

Source wording: “29% and runtimes by 40%”

Source E3
robots1 robotsBasis not reported

Source wording: “for a particular robot and task”

Source E4
Source excerpts and review record

No manual editorial approval recorded.

Original source quotation: “Beats Cosmos 3 Nano (16B) on RoboLab-120 (42.9% vs. 36.8%)”

Source E1

Original source quotation: “It outperforms the previous best open model by 6.1 percentage points”

Source E2

Original source quotation: “pairing F3A with high-level reasoners cuts execution costs by 29% and runtimes by 40%”

Source E3

Original source quotation: “Teams can fine-tune FLUX 3 Action on their own demonstrations to create policies for a particular robot and task”

Source E4

Original source quotation: “We’re releasing the weights, code, fine-tuning recipe, benchmarks, and reproducible examples”

Source E5
Text

FLUX 3 Action by @bfl_ai is super cool!
Everyone is pivoting into a robotics neolab

F3A is SOTA without the WAM latency: Beats Cosmos 3 Nano (16B) on RoboLab-120 (42.9% vs. 36.8%) while running at less than half the parameter count (7B) and up to 3.95x faster.

Flow matching tuned for physical control: Uses separate noise-to-signal transitions for video vs. actions and asymmetric classifier-free guidance, completely avoiding the need to decouple action heads from video synthesis.

And notably, pairing F3A with high-level reasoners cuts execution costs by 29% and runtimes by 40% over pure reasoning. This lets the fast policy handle motor primitives and calling the LLM only to correct course.

Quoted postBlack Forest Labs@bfl_ai
Introducing FLUX 3 Action. An open weights 7B World Action Model that achieves first place on the RoboLab benchmark. It outperforms the previous best open model by 6.1 percentage points while using 56% fewer parameters and running up to 3.95x faster.⁠⁠ FLUX 3 Action removes the usual trade-off between world action model performance and VLA speed: it still predicts video and actions together, but plans more than twice as far ahead and runs faster per second of robot motion than the strongest open VLA. Teams can fine-tune FLUX 3 Action on their own demonstrations to create policies for a particular robot and task. Together with @nvidia, we also integrated FLUX 3 Action natively into @huggingface's LeRobot, with fine-tuning recipes included and edge deployment on NVIDIA Jetson. Beyond robotics, we’re also seeing promising results training task-specific policies for acting in simulated environments like gaming, controlling a vehicle, computer use, and wherever else a model needs to understand a visual environment and then choose what to do next. FLUX 3 Action builds on the same image, video, and audio pretraining as FLUX 3, but uses a smaller architecture designed for practical deployment. In midtraining, we trained the model to predict actions and future frames together. We’re releasing the weights, code, fine-tuning recipe, benchmarks, and reproducible examples so researchers and developers can build on the model with their own robots, environments, and tasks (see below).
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Source:Animesh Garg · x.com