#RLHF

'Tinker is a Python API that exposes low-level training primitives so you can run custom loops locally while the platform handles distributed execution; it focuses on LoRA adapters, portable weights, and managed GPU clusters.'

'MoonshotAI released checkpoint-engine, a middleware that updates model weights across thousands of GPUs in about 20 seconds, enabling fast RL and large-scale LLM serving with minimal downtime.'

'OpenAI research shows hallucinations are statistical consequences of pretraining and that binary benchmarking incentivizes overconfident guessing; changing evaluation to reward calibrated uncertainty can reduce hallucinations.'

Anthropic AI proposes a novel method using persona vectors to detect and control personality shifts in large language models, enhancing their reliability and safety.

'Rubrics as Rewards (RaR) introduces a reinforcement learning approach that uses structured rubrics as reward signals, improving language model training in complex domains like medicine and science.'

SynPref-40M introduces a huge new preference dataset, enabling the Skywork-Reward-V2 family of models to achieve state-of-the-art results in human-AI alignment across multiple benchmarks.

Internal Coherence Maximization (ICM) introduces a novel label-free, unsupervised training framework for large language models, achieving performance on par with human-supervised methods and enabling advanced capabilities without human feedback.

Matthew Fitzpatrick, CEO of Invisible Technologies, shares insights on combining human expertise and AI to scale automation, the importance of RLHF, and trends driving AI ROI in enterprises.

OpenPipe’s ART·E uses reinforcement learning to deliver faster, cheaper, and more accurate email question-answering, outperforming OpenAI’s o3 agent in key metrics.