LLM News

Researchers introduce SOLACE, a post-training framework that replaces external reward models with an internal self-confidence signal derived from how accurately a text-to-image model recovers injected noise. The method enables fully unsupervised optimization and shows measurable improvements in compositional generation, text rendering, and text-image alignment.

Researchers propose Perception-R1, a method that adds visual perception reward signals to reinforcement learning training for multimodal AI models. The approach achieves state-of-the-art results on multiple reasoning benchmarks using just 1,442 training examples by explicitly teaching models to accurately perceive visual content before reasoning about it.