A researcher claims to have developed Jev, a new frontier AI model trained using RLCD methodology that is significantly faster and cheaper than existing models, with free output tokens and optimized for decision-making tasks.
Diogo Almeida and team released Jev, a new frontier AI model trained using RLCD methodology, which outperforms Astra, Fable, and Opus on radiofrequency engineering tasks with claimed 20-200x faster performance.
TypeSafe released Jev, an early-access model that answers structured questions with calibrated probabilities rather than generating text. The model uses RLCD training instead of RLHF to optimize for reliable predictions, costs $0.042 per million input tokens, and was tested on 24 Norwegian documents about salmon farming and tax policy.
TypeSafe AI develops Machine Native Intelligence, a production-focused AI system optimized for narrow, inspectable decisions in software rather than general-purpose responses. It introduces RLCD (Reinforcement Learning from Calibrated Decisions), a post-training approach that returns probabilistic decisions with reliable confidence scores instead of generated text, addressing limitations of RLHF like hallucinations and mode collapse.
TypeSafe AI has developed System One Models, a new class of AI systems built with Reinforcement Learning for Calibrated Decisions (RLCD) that produce typed outputs with confidence estimates rather than text. Unlike RLHF-trained language models optimized for human preferences, these models are designed for machine automation, offering reliability, speed, and type-safety at significantly lower cost than traditional LLMs.
TypeSafe AI introduces System One Models, a new class of AI designed for machine automation rather than chat. Using Reinforcement Learning for Calibrated Decisions (RLCD), these models produce typed decisions with confidence estimates, enabling reliable autonomous workflows without hallucinations.
TypeSafe AI introduces Jev, a new class of System One Models designed for machine automation rather than chat. Unlike RLHF-trained language models, Jev produces typed decisions with calibrated confidence estimates, enabling reliable autonomous workflows at significantly lower cost and latency.
A developer releases Qwen-2.5-1B-RLCD, an open-source model enabling 5x faster on-device JSON inference without new training. The same developer also announces Jev, a frontier AI model developed over 2 years using a new training method (RLCD) that claims 20-200x performance improvements.