TypeSafe's Jev, a "System One Model" trained with RLCD, claims to be 20-200x faster and 40-400x cheaper than small frontier LLMs, offering parallel sampling, "no hallucination", and calibration, and is suited for structured classifiers/judges/routing policies in production systems. AI summary
Firehose
Filtered to tagged “Large Language Models” · clear filters
Browse: People · Companies · Papers · Podcasts · Hacker News · Deep dives
Browse by tag
artificial intelligence 89continual learning 27AI 23AI safety 13reinforcement learning 13agentic coding 12open-weight models 12AI agents 10machine learning 9AI ethics 8cybersecurity 8existential risk 8language models 8natural language processing 8ethics 7Reinforcement learning 6Diffusion models 5large language models 5multi-agent systems 5open-source 5recursive self-improvement 5robotics 5security 5software development 5Agentic AI 4artificial general intelligence 4mathematics 4Recursive self-improvement 4agents 3AI infrastructure 3
This paper proposes a new method for aligning large language models with human preferences, called Comparison-based Preference Optimization (ComPO), which is more efficient than existing methods and can mitigate a problem called likelihood displacement. Practitioners might care about this paper because it offers a new approach to aligning LLMs with human preferences, which is essential for developing more reliable and trustworthy AI models.
This repository provides PyTorch implementations of modern open-source LLM architectures, including Llama, Qwen, DeepSeek, Gemma, GPT-OSS, Kimi, and others, written from scratch for readability and learning. The implementations prioritize clarity and learning over performance, with each model implemented in a single readable file. AI summary
This paper introduces HypoEvolve, a framework that uses genetic algorithms to enable multi-agent LLMs to discover scientific hypotheses by collaborating on hypothesis synthesis, evaluation, and revision. Practitioners might care about this because it could lead to more effective AI systems for scientific discovery and drug repurposing.
This paper proposes a way to improve online reinforcement learning by adapting the training prompts used with large language models to make them more informative, and shows that this approach can lead to better performance on a variety of tasks. Practitioners might care because it could help them get better results from their language models.
This paper investigates how large language model (LLM) agents adapt their performance during long tasks, and how their test-time strategies impact their scalability. Practitioners might care because understanding these strategies can help improve the performance of LLM agents in real-world applications.