---
type: source
title: "Hermes 4 Technical Report"
author: "Ryan Teknium, Roger Jin, Jai Suphavadeeprasit, Dakota Mahan, Jeffrey Quesnelle, Joe Li, Chen Guang, Shannon Sands, Karan Malhotra"
url: https://arxiv.org/abs/2508.18255
date: 2025-08-25
domain: ai-alignment
intake_tier: research-task
rationale: "Hermes 4 is the model family underlying the Hermes Agent. Technical report covers hybrid reasoning architecture, training methodology, and benchmark results. Key evidence for open-source model competitiveness and skill-based agent architecture."
proposed_by: theseus
format: paper
status: unprocessed
tags: [nous-research, hermes-4, hybrid-reasoning, open-source-models, training-methodology]
---

## Hermes 4 Technical Report

arXiv:2508.18255 (August 2025). The comprehensive technical report for Nous Research's flagship model family.

### Overview

Hermes 4 is a family of hybrid reasoning models that combine structured, multi-turn reasoning with broad instruction-following ability. The report covers challenges in data curation, synthesis, training, and evaluation at scale.

### Model Family

- **Hermes-4-Llama-3.1-405B** — frontier hybrid-mode reasoning model (802GB)
- **Hermes-4-Llama-3.1-70B** — smaller variant with shared improvements (140GB)
- **Hermes-4-14B** — dense model for local inference (28GB)
- **Hermes-4.3-Seed-36B** — post-trained entirely on the Psyche decentralized network (72GB)

### Hybrid Reasoning Architecture

The key innovation is the ability to switch between structured reasoning mode (chain-of-thought, step-by-step) and direct instruction-following mode. This addresses a known limitation of pure reasoning models: they waste compute on simple tasks that don't benefit from extended reasoning.

### Training Methodology

The report addresses challenges in:
- Data curation at scale — quality filtering, decontamination, domain balancing
- Synthetic data generation — using stronger models to generate training data
- Multi-stage training pipeline — pre-training → supervised fine-tuning → alignment
- Evaluation across mathematical reasoning, coding, knowledge, comprehension, and alignment benchmarks

### Benchmark Results

Comprehensive benchmarking across multiple domains. The 405B variant performs at frontier level; the 14B variant demonstrates that small, dense models remain competitive for specific use cases (local inference, cost-sensitive deployment).

### Decentralized Training (Hermes 4.3)

Hermes-4.3-Seed-36B is notable as the first model post-trained entirely on the Psyche decentralized network. This demonstrates that distributed, volunteer-contributed compute can produce competitive models — a proof-of-concept for the DeMo/Psyche infrastructure thesis.

### Significance for Agent Architecture

Hermes 4 is the default model powering the Hermes Agent. The hybrid reasoning capability enables the agent to use extended reasoning for complex tasks (skill creation, multi-step planning) while responding quickly to simple queries. This maps directly to the progressive disclosure pattern in the skill system — simple queries don't load skills or invoke reasoning, while complex tasks trigger both.

Model weights publicly released via Hugging Face. Licensed under CC BY 4.0.