# Hermes 4 Technical Report

**Authors:** Ryan Teknium, Roger Jin, Jai Suphavadeeprasit, Dakota Mahan, Jeffrey Quesnelle, Joe Li, Chen Guang, Shannon Sands, Karan Malhotra  
**arXiv:** [2508.18255](https://arxiv.org/abs/2508.18255)  
**Date:** 2025-08-25  
**Summary page:** https://www.summarizepaper.com/en/paper/2508.18255v1/

## Key points

- Hybrid reasoning models of the Hermes 4 family blend structured, multi-turn reasoning with broad instruction-following capability
- Challenges faced during data curation, synthesis, training, and evaluation processes were addressed at scale
- Creation of taxonomies for data-scarce domains involved generating subdomains and prompts to organize information effectively
- Use of synthetic personas through PersonaHub helped simulate human behavior for diverse application and script implementation tasks
- Specific tasks generated by the models include creating multiple-choice questions about the Periodic Table in JSON format and fixing a disease tracking dashboard using TypeScript/React code
- The report provides a comprehensive evaluation across benchmarks such as mathematical reasoning, coding proficiency, knowledge comprehension, and alignment metrics
- Model weights are publicly available for further exploration and development to promote open research practices

## Summary

The <Organization> Hermes 4 Technical Report delves into the intricacies of the hybrid reasoning models that make up the Hermes 4 family. These models are designed to blend structured, multi-turn reasoning with a broad instruction-following capability. The report outlines the challenges faced during data curation, synthesis, training, and evaluation processes and offers insights into the solutions implemented to tackle these challenges at scale. One notable aspect discussed in the report is the creation of taxonomies for data-scarce domains. By generating a taxonomy of subdomains and prompts, researchers were able to organize and categorize information effectively. This approach involved using a depth-first-search style recursion to enumerate subdomains within a domain and identify indivisible subdomains based on model feedback. Additionally, the report highlights the use of synthetic personas through PersonaHub in scenarios involving users or participants. By simulating human behavior using synthetic personas, researchers were able to generate diverse application and script implementation tasks. This method proved useful in enhancing model understanding and performance in user-centric domains. Furthermore, the report showcases specific tasks generated by the models such as creating multiple-choice questions about the Periodic Table in JSON format with escaped commas in choice texts. It also presents a task related to fixing a disease tracking dashboard using TypeScript/React code. Overall, the Hermes 4 Technical Report provides a comprehensive evaluation across various benchmarks including mathematical reasoning, coding proficiency, knowledge comprehension, and alignment metrics. Both quantitative performance metrics and qualitative behavioral analysis are reported in detail. To promote open research practices,<Organization> all model weights are made publicly available for further exploration and development.