🚀 Introducing Hy-Nomas: An Open Source Free-Form Interactive Video World Model Key Features: * Free-form interaction with text prompts and camera trajectories * Long-horizon generation with world memory * Lightweight LoRA adaptation for specialized domains such as robotics * Optimized for Inference on Consumer GPUs Test it and drop feedback anytime！ 🔗 Website: 💻 GitHub: 🤗 Hugging Face: 🎥 Demo: #Hy##WorldModels# #VideoGeneration# #GenerativeAI#

🚨Andrew Tate on how they ALTERED covid history. 😷 “I was walking around with no mask telling people to get f*cked. It was fine.” “Look it up in 100 years — that’s not what happened.”

A world model you steer with text and camera controls, with a design that even runs on a single RTX 4090 🎮 Hy-Nomas: Towards Free-form Interactive World Generation An 8B autoregressive video world model from a Tencent team. One model handles free-form text, continuous camera control, and multi-turn memory. Three highlights: 🧠 Action-aware causal attention Each segment's text is split into a description that later segments can see and actions confined to their own time window. The aim is to curb action-order errors and repeated actions across segments. ⚡ 4-step distillation and light inference Sampling drops from 100 to 4 function evaluations, combined with INT8 and sparse attention. A 100-second video (1280x704, 24 FPS) is generated at a peak of 20.53 GiB on an RTX 4090. 📊 Strong lead on text control In human evaluation of prompt following, it wins 75.0% and loses 8.3% against LingBot-World v2. Spatial memory, however, still trails Genie 3. A good read for both the design and evaluation of interactive world generation. #WorldModels# #VideoGeneration#

In RL training, a vLLM rollout engine and a Megatron trainer can run the same policy yet disagree on a token's logprob due to floating-point non-associativity. SkyRL's IsoExec combines an execution contract with a unified model, aligning rounding-sensitive execution choices across rollout and training. Bitwise parity holds across different TP, EP, and SP layouts. For Gated DeltaNet, the chunkwise-parallel recurrent algorithm makes parallel training and prefill bitwise identical to recurrent decode. Qwen3.5-35B-A3B, DAPO, 8xH100, 50 steps: logprob diff 1.6e-2 to 6.7e-7, full-step overhead 25.3% ✅ vLLM's scheduler and CUDA graphs still apply. Thanks to @JiangAlexander1 and the SkyRL team at @NovaSkyAI. 🔗