Hermes 4 Technical Report
This work addresses the problem of enhancing AI reasoning and instruction-following capabilities for researchers and developers, but it appears incremental as it builds on existing hybrid approaches without claiming major breakthroughs.
The authors tackled the challenge of developing hybrid reasoning models by creating Hermes 4, which combines structured reasoning with instruction-following, and they reported comprehensive evaluation across multiple benchmarks including mathematical reasoning and coding, with all model weights published publicly.
We present Hermes 4, a family of hybrid reasoning models that combine structured, multi-turn reasoning with broad instruction-following ability. We describe the challenges encountered during data curation, synthesis, training, and evaluation, and outline the solutions employed to address these challenges at scale. We comprehensively evaluate across mathematical reasoning, coding, knowledge, comprehension, and alignment benchmarks, and we report both quantitative performance and qualitative behavioral analysis. To support open research, all model weights are published publicly at https://huggingface.co/collections/NousResearch/hermes-4-collection-68a731bfd452e20816725728