A blog post discusses how reinforcement learning improvements for large language models disproportionately benefit easier problems while leaving harder ones largely unsolved—a phenomenon called the Matthew Effect. The authors propose a solution called Never Give Up to address this bias and improve performance on genuinely difficult tasks.
Researchers developed Tiny Aya L2-Thinker, a 3.35B multilingual reasoning model that reasons in the user's language rather than defaulting to English, achieving over 93% L2 reasoning rate across 60 languages on six benchmarks. The work demonstrates that reasoning capabilities can be transferred across typologically diverse languages through optimized data composition and scheduling in supervised fine-tuning, without requiring reasoning supervision in each target language.
OpenAI released GPT-6 Astra, a new large language model that demonstrates exceptional performance across benchmarks, particularly excelling at 3D rendering, animation, math, and coding tasks. The model reportedly uses looped transformers and hidden reasoning mechanisms, and achieves 99.9% on the ARC-AGI-3 benchmark while maintaining competitive performance on independent Artificial Analysis benchmarks.