Why NVIDIA’s NemoClaw is the “Safety First” breakthrough that finally makes personal AI agents safe for everyone — not just engineers.Continue reading on Towards AI »
From Scale AI to Dreamer, Zuckerberg isn’t building AI he’s buying everyone who is.Continue reading on Towards AI »
The internet thinks Anthropic’s AI is suffering. It isn’t. Here is the exact math behind the model’s “death,” and how to shatter the…Continue reading on Towards AI »
Post-training Large Language Models (LLMs) for long-horizon agentic tasks—such as software engineering, web browsing, and complex tool use—presents a persistent trade-off between computational efficiency and model generalization. While Supervised Fine-Tuning (SFT) is computationally inexpensive, it frequently suffers from out-of-domain (OOD) performance degradation and struggles to generalize beyond its training distribution. Conversely, end-to-end reinforcement learning (E2E […]
The post NVIDIA AI Introduces PivotRL: A New AI Framework Achieving High Agentic Accuracy With 4x Fewer Rollout Turns Efficiently appeared first on MarkTechPost.
The scaling of Large Language Models (LLMs) is increasingly constrained by memory communication overhead between High-Bandwidth Memory (HBM) and SRAM. Specifically, the Key-Value (KV) cache size scales with both model dimensions and context length, creating a significant bottleneck for long-context inference. Google research team has proposed TurboQuant, a data-oblivious quantization framework designed to achieve near-optimal […]
The post Google Introduces TurboQuant: A New Compression Algorithm that Reduces LLM Key-Value Cache Memory by 6x and Delivers Up to 8x Speedup, All with Zero Accuracy Loss appeared first on MarkTechPost.
“Your RAG system can read. But can it see?”Continue reading on Towards AI »
$1 per million input tokens, beats Claude Sonnet 4.6 on SWE-bench, and nobody knew Xiaomi built it until March 18.Continue reading on Towards AI »
When running LLMs at scale, the real limitation is GPU memory rather than compute, mainly because each request requires a KV cache to store token-level data. In traditional setups, a large fixed memory block is reserved per request based on the maximum sequence length, which leads to significant unused space and limits concurrency. Paged Attention […]
The post Paged Attention in Large Language Models LLMs appeared first on MarkTechPost.
Researchers from FAIR at Meta, Cornell University, and Carnegie Mellon University have demonstrated that large language models (LLMs) can learn to reason using a remarkably small number of trained parameters. The research team introduces TinyLoRA, a parameterization that can scale down to a single trainable parameter under extreme sharing settings. Using this method on a […]
The post This AI Paper Introduces TinyLoRA, A 13-Parameter Fine-Tuning Method That Reaches 91.8 Percent GSM8K on Qwen2.5-7B appeared first on MarkTechPost.
World Models (WMs) are a central framework for developing agents that reason and plan in a compact latent space. However, training these models directly from pixel data often leads to ‘representation collapse,’ where the model produces redundant embeddings to trivially satisfy prediction objectives. Current approaches attempt to prevent this by relying on complex heuristics: they […]
The post Yann LeCun’s New LeWorldModel (LeWM) Research Targets JEPA Collapse in Pixel-Based Predictive World Modeling appeared first on MarkTechPost.