#reinforcement learning

Researchers from General Intuition, Kyutai, and Epic Games released MIRA, a 5B parameter interactive world model trained on 10k hours of synthetic Rocket League data, enabling real-time 4-player gameplay at 20 FPS on a single B200 GPU. MIRA demonstrates the feasibility of large-scale world models for multiplayer, physically complex environments, potentially accelerating research in game AI, simulation, and reinforcement learning by providing a playable demo and open-source tools. The model uses a latent diffusion architecture to generate video frames conditioned on all four players' actions, and the team released a 1,000-hour dataset of 4-player gameplay, a technical report, and a playable online demo.

LingBot-Video is a 13B parameter sparse-MoE video diffusion transformer (1.4B active) that has been post-trained with reinforcement learning using six rewards, including a physical-plausibility reward, and supports action-conditioned world model prediction for robot rollouts. This work advances video generation and world modeling by combining sparse MoE for efficiency, RL post-training for improved plausibility, and open release of weights and code, making it a strong candidate for robotics and video synthesis research. The model uses a DeepSeek-V3-style sparse MoE with 128 experts and top-8 routing, achieving top average performance on RBench but ranking second on general text-to-video. The physical-plausibility reward is judged by a VLM, which raises concerns about reward hacking despite adding real-video negatives.

On June 29, Tesla released FSD v14 Lite, which distills HW4-level V14 intelligence onto HW3 hardware, enabling HW3 vehicles to access reinforcement learning and offline models previously exclusive to HW4. The update also introduces parking, unparking, and reversing functions for the first time. This update significantly extends the lifespan of older HW3 vehicles by bringing cutting-edge autonomous driving capabilities to millions of existing owners without hardware upgrades. It demonstrates Tesla's engineering prowess in model distillation and reinforces its commitment to continuous software improvement across hardware generations. FSD v14 Lite improves navigation, lane merging, pedestrian interaction, traffic light handling, and cut-in responses, while reducing unnecessary braking and enhancing steering smoothness and lane centering. The update also adds full-time speed profile customization for driving style personalization.