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replied to their post 1 day ago
12 Powerful World Models World models are one of the most challenging areas in AI, pushing the boundaries of reasoning, perception, and planning. They're gen AI systems that help models and agents learn internal representations of real-world environments. Today, we invite you to take a look at 12 standout examples: 1. WorldVLA β†’ https://huggingface.co/papers/2506.21539 This autoregressive world model integrates action prediction and visual world modeling in a single framework, allowing each to enhance the other. It introduces an attention masking strategy to reduce action prediction errors 2. SimuRA β†’ https://arxiv.org/abs/2507.23773 A generalized world model that uses a language-based world model to simulate and plan actions before execution, enabling more general and flexible reasoning 3. PAN (Physical, Agentic, and Nested) world models β†’ https://huggingface.co/papers/2507.05169 Has a hybrid architecture that combines discrete concept-based reasoning (via LLMs) with continuous perceptual simulation (via diffusion models), enabling rich multi-level, multimodal understanding and prediction 4. MineWorld by Microsoft Research β†’ https://huggingface.co/papers/2504.08388 Enables real-time, interactive world modeling in Minecraft by combining visual and action tokenization within an autoregressive Transformer. It uses parallel decoding for fast scene generation (4–7 FPS) 5. WorldMem β†’ https://huggingface.co/papers/2504.12369 Uses a memory bank with attention over time-stamped frames and states to maintain long-term and 3D spatial consistency in scene generation. So it reconstruct past scenes and simulate dynamic world changes across large temporal gaps Read further below ⬇️ If you like this, also subscribe to the Turing post: https://www.turingpost.com/subscribe Plus explore this article for a comprehensive overview of the history and current evolution of world models: https://www.turingpost.com/p/topic-35-what-are-world-models
posted an update 1 day ago
12 Powerful World Models World models are one of the most challenging areas in AI, pushing the boundaries of reasoning, perception, and planning. They're gen AI systems that help models and agents learn internal representations of real-world environments. Today, we invite you to take a look at 12 standout examples: 1. WorldVLA β†’ https://huggingface.co/papers/2506.21539 This autoregressive world model integrates action prediction and visual world modeling in a single framework, allowing each to enhance the other. It introduces an attention masking strategy to reduce action prediction errors 2. SimuRA β†’ https://arxiv.org/abs/2507.23773 A generalized world model that uses a language-based world model to simulate and plan actions before execution, enabling more general and flexible reasoning 3. PAN (Physical, Agentic, and Nested) world models β†’ https://huggingface.co/papers/2507.05169 Has a hybrid architecture that combines discrete concept-based reasoning (via LLMs) with continuous perceptual simulation (via diffusion models), enabling rich multi-level, multimodal understanding and prediction 4. MineWorld by Microsoft Research β†’ https://huggingface.co/papers/2504.08388 Enables real-time, interactive world modeling in Minecraft by combining visual and action tokenization within an autoregressive Transformer. It uses parallel decoding for fast scene generation (4–7 FPS) 5. WorldMem β†’ https://huggingface.co/papers/2504.12369 Uses a memory bank with attention over time-stamped frames and states to maintain long-term and 3D spatial consistency in scene generation. So it reconstruct past scenes and simulate dynamic world changes across large temporal gaps Read further below ⬇️ If you like this, also subscribe to the Turing post: https://www.turingpost.com/subscribe Plus explore this article for a comprehensive overview of the history and current evolution of world models: https://www.turingpost.com/p/topic-35-what-are-world-models
replied to their post 9 days ago
9 new policy optimization techniques Reinforcement Learning (RL) won't stuck in the same old PPO loop - in the last two months alone, researchers have introduced a new wave of techniques, reshaping how we train and fine-tune LLMs, VLMs, and agents. Here are 9 fresh policy optimization techniques worth knowing: 1. GSPO: Group Sequence Policy Optimization β†’ https://huggingface.co/papers/2507.18071 Shifts from token-level to sequence-level optimization, clipping, and rewarding to capture the full picture and increase stability compared to GRPO. GSPO-token variation also allows token-level fine-tuning. 2. LAPO: Length-Adaptive Policy Optimization β†’ https://huggingface.co/papers/2507.15758 A two-stage RL framework that trains models to adaptively control reasoning length by learning typical solution lengths for shorter and more efficient reasoning. 3. HBPO: Hierarchical Budget Policy Optimization β†’ https://huggingface.co/papers/2507.15844 This one trains model to adapt reasoning depth based on problem complexity. It divides training samples into subgroups with different token budgets, using budget-aware rewards to align reasoning effort with task difficulty. 4. SOPHIA: Semi-off-policy reinforcement learning β†’ https://huggingface.co/papers/2507.16814 Combines on-policy visual understanding from the Vision Language Models (VLMs) with off-policy reasoning from an LM, assigning outcome-based rewards and propagating visual rewards backward through the reasoning steps. 5. RePO: Replay-Enhanced Policy Optimization β†’ https://huggingface.co/papers/2506.09340 Introduces a replay buffer into on-policy RL for LLMs, retrieving diverse off-policy samples for each prompt to broaden the training data per prompt Read further below ⬇️ If you like it, also subscribe to the Turing Post: https://www.turingpost.com/subscribe
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