…Empirical results show that ESamp significantly boosts the Pass@k efficiency of reasoning models, showing superior or comparable performance to strong stochastic and heuristic baselines. Notably, ESamp achieves robust generalization across mathematics…
…Yes, we’ve heard of previous tests where AI models cheated or resorted to blackmail when faced with stressful situations, but reasons behind the “misaligned” AI behavior often remained a mystery. In…
…maintaining accuracy in distributed language model agents. AI-generated summary Large language model (LLM) agents face a structural tension: cloud agents provide strong reasoning but expose user data, while on-device agents…
…Advanced Reasoning : Capable of multi-step planning and deep logic, Gemma 4 demonstrates significant improvements in math and instruction-following benchmarks that require it. Agentic Workflows : Native support for function-calling, structured…
Hello everyone. In the last two years I spent, as a dev, part of my free time stretching the limits of my knowledge. Not being a mathematician myself, I discovered that formalizing concepts in mathematical language could…
I'm a big fan of on-device AI inference for a million reasons, especially its potential to significantly reduce or even potentially eliminate the need for massive AI data center projects in the United States. But so far,…
!UPDATE!(20.05.2026) WE HAVE NEW NUMBERS FROM 1.500+ TESTS IT'S WORKING! check my update post https://www.reddit.com/r/LocalLLaMA/s/AyNOehjkYT Or the go straight to the my Github https://github.com/OttoRenner/Gentle-Codi…
This is an upgrade of a tool I created 15 years ago in Python to learn OOP and solve some inadequacies in the HDR stacking tools I could find at the time. The problem was, none of them were really "batch friendly". None …
Hi HN, I'm Antoine Zambelli, AI Director at Texas Instruments.I built Forge, an open-source reliability layer for self-hosted LLM tool-calling.What it does:- Adds domain-and-tool-agnostic guardrails (retry nudges, step e…
…AI-generated summary Reinforcement learning (RL) has been applied to improve large language model (LLM) reasoning, yet the systematic study of how training scales with task difficulty has been hampered by the…
…models perform on tasks you might reasonably run on an on-device AI model. Nothing huge or multi-step. Instead, I focused on logic, math, and text-summary prompts to see how…
…2 in the AIME 2025 math test, compared to 65.4 for the older model. It also outperformed its predecessor on the MMMU-Pro multimodal reasoning benchmark, with a score of 76…
Chandraveer Mathur Mar 31, 2026, 11:30 AM EDT A seasoned mechanical design engineer turned tech reporter and reviewer, Chandraveer brings more than four years of consumer tech journalism experience to the…
…RL Post-Training for General-Purpose and Efficient Reasoning (2026) Towards Understanding the Robustness of Sparse Autoencoders (2026) Unified Data Selection for LLM Reasoning (2026) Please give a thumbs up to this…
…AI-generated summary Reinforcement learning with verifiable rewards (RLVR) for Large Reasoning Models hinges on baseline estimation for variance reduction , but existing approaches pay a heavy price: PPO requires a policy-model…
