Another really interesting paper from my 2025 bookmarked papers: On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Language Models (https://arxiv.org/abs/2512.07783). In short, RL is most effective when applied to data that is neither too close to nor too far from the
MACHINE LEARNING
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FlowBlending: Stage-Aware Multi-Model Sampling for Video Generation
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FlowBlending Stage-Aware Multi-Model Sampling for Fast and High-Fidelity Generation
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Dream2Flow: Video Generation with 3D Object Flow for Manipulation
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Dream2Flow
— AK (@_akhaliq) 2 janvier 2026
Bridging Video Generation and Open-World Manipulation with 3D Object Flow pic.twitter.com/5m09xRzjG0Dream2Flow Bridging Generation and Open-World Manipulation with 3D Object Flow
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Hypergraph Memory Enhances Multi-step RAG Long-Context Modeling
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Improving Multi-step RAG with Hypergraph-based Memory for Long-Context Complex Relational Modeling
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DiffThinker: Generative Multimodal Reasoning with Diffusion Models
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DiffThinker Towards Generative Multimodal Reasoning with Diffusion Models
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Dynamic Large Concept Models with Latent Reasoning in Adaptive Semantic Space
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Dynamic Large Concept Models Latent Reasoning in an Adaptive Semantic Space
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JavisGPT: Multi-modal LLM for Video Understanding and Generation
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JavisGPT A Unified Multi-modal LLM for Sounding-Video Comprehension and Generation
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The Efficiency Era of AI Models
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The lottery ticket hypothesis wasn't wrong. We just weren't ready for it. In 2025, sparse models are no longer academic curiosities. They're production infrastructure. The future isn't bigger models. It's smarter pruning. Welcome to the efficiency era. Read it here if you
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Implementing Sparse Model Training Today
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How to implement this today: 1. Use PyTorch's torch.nn.utils.prune for magnitude pruning
2. Apply 2:4 structured patterns for GPU acceleration
3. Fine-tune with sparse-aware training
4. Deploy with TensorRT or ONNX Runtime The infrastructure exists. Most teams just don't know -
Sparse models generalize better than dense ones
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The counterintuitive part: Sparse models often generalize BETTER than dense ones. Why? Dense networks memorize noise. Sparse networks are forced to learn robust features. It's not just cheaper. It's actually better science.