LiteC-WAM:A: A Lightweight Cross-Task and -Embodiment World Action Model

World action models (WAMs) aim to generate executable actions and predict their effects on the environment. However, key challenges remain in maintaining a lightweight model footprint, transferring learned dynamics across tasks, and adapting to new robots without forgetting previously learned capabilities. We introduce LiteC-WAM, a lightweight cross-task and cross-embodiment WAM comprising three key components: (1) a lightweight shared backbone that couples a light perception module, sparse Top-2 dynamics experts, and a flow-matching action head refined by world-model feedback; (2) cross-task adapters that transfers shared dynamics without backbone retraining; and (3) low-rank observation adapters paired with robot-specific action encoders and decoders. For lightweight deployment, LiteC-WAM achieves a 96.0% success rate with only 47.57M parameters on 5 representative LIBERO tasks, outper-forming other lightweight policies such as Diffusion Policy ( 65M, 72.4%), Octo-Base (93M, 75.1%), and SmolVLA-240M (240M, 82.75%). Its success is also comparable to billion-parameter models, such as π0 (3.3B, 94.2%), LingBot-VA (5.3B, 98.5%), and OpenVLA-OFT (7B, 97.1%). For cross-task transfer, LiteC-WAM achieves 86.67% across 15 RLBench manipulation tasks using cross-task adapters guided by language instructions, without retraining the shared backbone. For cross-embodiment transfer, LiteC-WAM achieves 82.09% closed-loop success on dual-arm SO-ARM101 and a 91.86% average offline pass rate when comparing predicted and recorded actions across seven RoboChallenge ARX5 tasks. Together, these results validate LiteC-WAM as an effective lightweight model for cross-task and cross-embodiment transfer. Evaluation code and protocols are available at https://anonymous.4open.science/r/LITEC-WAM-A-LIGHTWEIGHTCROSS-TASK-AND-EMBODIMENT-WORLD-ACTION-MODEL-632F/