Yeah, there is a silver lining. But I think it gets a bit muddled since most base models are trained on CoT data.
LLMS
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Optimal RL Balance in Training Reasoning Models
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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
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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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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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Instagram evolves for AI as DeepSeek hints at next-gen architecture
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Top stories in AI today: – IG head platform must “evolve fast” for AI
– DeepSeek hints at next-gen AI architecture
– Use Codex to write code on the web
– OAI overhauling audio for upcoming device
– 4 new AI tools, community workflows, and more Read more: https://
therundown.ai/p/instagrams-a
i-driven-identity-crisis
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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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Sparse Models Deliver Real-World Gains
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Real-world results from companies already deploying sparse models: OpenAI: 40% cost reduction on GPT-4 API
Meta: 3x throughput increase for Llama inference
Google: 60% memory savings for production transformers The early adopters are already winning. -
2026 Breakthroughs Made AI Production-Ready
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Three breakthroughs made this production-ready in 2026: 1. Pruning-aware training (train sparse from the start)
2. Hardware support (NVIDIA Ampere+, Apple Neural Engine)
3. Framework integration (PyTorch 2.0 native sparsity) The tooling finally caught up to the theory. -
Neural networks are 90% redundant by design
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The academic papers missed the real story. It's not about finding "winning tickets" in random initialization. It's about discovering that neural networks are 90% redundant by design, and modern hardware finally lets us exploit that. Evolution over-parameterizes. We can prune.