Key takeaways

• Hybrid reasoning, visible on demand: Hermes 4 offers chain‑of‑thought transparency you can toggle, enabled by a synthetic data pipeline and verification suite (DataForge / Atropos video).
• Training choices matter: long traces, multi‑path solutions, and a dedicated “when to stop” fine‑tune reduce runaways while preserving most accuracy (training details).
• Open‑weight, production‑ready: Hermes 4 models (14B, 70B, 405B) are available for inspection and custom finetuning (openrouter, Hugging Face).
• Complementary approach — Google RLM: RLM reframes regression as text‑to‑text prediction for system telemetry with fast adaptation and strong accuracy (Google Research blog).
• Practical pairing: use Hermes 4 for explainable plans and proofs; use RLM for numeric forecasts and ranking from structured telemetry.

Table of contents

1. Context: Why Hermes 4 matters now
2. What is the Hermes 4 LLM?
3. How hybrid reasoning & chain‑of‑thought are taught
4. DataForge synthetic training data
5. Atropos verifiers & quality control
6. Training discipline: teaching “when to stop”
7. Benchmarks and performance
8. Training engineering & hardware
9. Practical implications & fits
10. Limitations & considerations
11. Google RLM: text‑to‑text regression
12. How Hermes 4 and RLM complement each other
13. Getting started & resources
14. Conclusion
15. FAQ

Context: Why Hermes 4 matters now

Open‑weight models are closing the gap on hard reasoning tasks. Teams can inspect, fine‑tune, and deploy high‑skill models without a closed API in the loop. Nous Research Hermes 4 arrives in this moment with a clear goal: hybrid reasoning you can see and steer, backed by open benchmarks. See the model entry on OpenRouter.

What is the Hermes 4 LLM?

Hermes 4 ships in three sizes: 14B, 70B, and 405B. The 405B “monster” is built on the Llama 3.1 405B backbone and then pushed through intense post‑training to improve reasoning quality. Read the 70B model card on Hugging Face and the 405B details on OpenRouter.

Core philosophy:

• Squeeze performance from post‑training, not just pretraining.
• Make reasoning visible and controllable.
• Enforce formats, schemas, and stop signals without killing accuracy.

Hybrid behavior: simple queries return short answers; hard problems can surface chain‑of‑thought inside tags such as <think> ... </think>. Toggle this mode with prompts. See a model listing on LM Studio.

How hybrid reasoning & chain‑of‑thought transparency are taught

Hermes 4 treats chain‑of‑thought as an internal trace. The model learns when to surface that trace using tags, making thinking steps first‑class rather than an accident of sampling. (Source material and demonstrations are available in the DataForge / Atropos video.)

Benefits:

• Auditability — check each step and find slips.
• Debugging — catch math or logic errors early.
• Pedagogy — students see the why, not just the what.

Guardrails: ask for “final answer only” and Hermes stays terse; wrap with reasoning tags and it lays out the path. Schema checks and stop signals ensure the output shape even when traces are long.

DataForge synthetic training data: the engine behind Hermes 4

Instead of scraping messy web text, Hermes 4 uses DataForge — a synthetic pipeline that builds diverse, high‑quality reasoning examples on purpose. Watch the pipeline description on YouTube.

Graph‑based transformations: samples are treated like graphs with inputs, requirements, outputs, and transformations (PDDL‑like planning). This lets the team compose hard tasks while preserving structure and checks.

Example pipeline:

1. Start with a Wikipedia article (e.g., photosynthesis).
2. Transform it (e.g., rewrite as a short rap with the key facts).
3. Decompose into Q&A, step‑by‑step explanations, and small quizzes.
4. Generate multiple valid reasoning traces to teach strategy diversity.

Scale and length: roughly 5 million samples (~19B tokens) with long traces up to 16k tokens, teaching the model to sustain reasoning.

Atropos reinforcement learning verifiers: quality control

Atropos is the verification gauntlet that stress‑tests samples and model outputs. It runs a battery of checks before data or outputs pass into training or CI. See the verification pipeline described in the video.

What runs in the gauntlet:

• 1,000+ verifiers (math, code, science, safety).
• Format checks across 150+ templates (JSON, YAML, tables).
• Instruction following tests, rubric scores, and schema validation (Pydantic).
• Tool‑use simulations to test agentic flows.

Multiple valid solution paths are preserved: if different traces solve the problem, both can pass. This teaches strategy diversity and reduces brittleness.

Tackling rambling: training Hermes 4 to stop at the right time

The challenge: long reasoning traces can run away and hit context limits. Hermes 4 adds a second fine‑tuning stage focused solely on closing and end‑of‑sequence signals.

Method:

• Generate ultra‑long traces and cut them (~30k tokens).
• Insert closing tags and end signals.
• Fine‑tune only on stopping tokens so “how to reason” and “when to stop” are learned separately.

Measured impact: large reductions in runaway generations (AIME’24 −78%, LiveCodeBench ~−80%), with accuracy held within ~5–12% depending on the benchmark. See the training overview on YouTube.

Benchmarks: open‑weight state‑of‑the‑art reasoning

The 405B Hermes 4 posts competitive open‑weight reasoning numbers across math, science, and code. Headline public results include (reported by the team):

• MATH500: 96.3%
• AIME’24: 81.9%
• AIME’25: 58.1%
• GPQA Diamond: 70.5%
• LiveCodeBench: 61.3%

RefusalBench (alignment style): Hermes 4 in reasoning mode reports 57.1% compared with lower numbers for some closed models, suggesting a neutral but auditable engagement policy (source).

Smaller variants (14B, 70B) inherit the same recipe; exact scores vary by size. See the 70B card on Hugging Face and the 405B page on OpenRouter.

Training engineering and hardware choices

Hermes 4 training used heavy compute (192 NVIDIA B200 GPUs) combined with careful engineering. See the overview on the project site: hermes4.nousresearch.com.

Efficiency tactics:

• Long sequences with minimal padding (token packing).
• Important‑token learning: focus updates where the signal is strong.
• Careful learning‑rate schedules.
• Stacked parallelism: mix data, tensor, and pipeline parallelism for smooth scaling.

The implication: with smart pipelines and verification, open teams can reach near‑frontier results and support private finetunes and reproducible research.

Practical implications: when to reach for Hermes 4

Hermes 4 is ideal when you need clear steps and control over output format. Good fits include:

• Math problem solving and proof checking
• Code reasoning, debugging, and test planning
• Scientific Q&A with citations and step lists
• Explainable tutoring and walkthroughs
• Agent workflows that must obey schemas and tool‑call patterns

Why transparency matters: audit trails for regulated domains, faster root‑cause analysis, and clearer teaching materials.

Open‑weight benefits: finetune on private corpora, deploy on your GPUs/VPCs, and inspect safety policies. Prompt tips: use terse mode for throughput and enable chain‑of‑thought for debugging. See model listings on OpenRouter and Hugging Face.

Limitations and considerations

Plan for trade‑offs:

• Some accuracy dips after strict “when to stop” training — teams trade a small accuracy loss for far fewer runaways (source).
• Context budgeting matters on 14B/70B variants — long traces consume tokens quickly (70B model card).
• Alignment stance is neutral and engaging; consider extra governance rules for sensitive domains (source).
• Inference cost: the 405B model is heavy. Use it where the benefit outweighs latency and cost (OpenRouter).

Pivot to Google RLM: Regression Language Model

Some tasks are about predicting system behavior rather than step‑by‑step reasoning. Google’s Regression Language Model (RLM) reframes this as text‑to‑text regression: you serialize system state as JSON/YAML and the model returns numeric predictions as text. See the Google Research overview at research.google/blog.

Why this matters:

• System telemetry is structured already — text serialization keeps pipelines simple.
• Long context allows rich histories or config dumps.
• Sample multiple predictions to gauge uncertainty.

RLM design: intentionally small (~60M parameters), trained directly on I/O pairs (no broad web pretraining). Key choices include custom number tokenization and very fast fine‑tune cycles — a few hundred examples can get you started. See the Google Research blog for details (source).

Performance: on Borg clusters RLM reports very high rank correlations (up to 0.99) and large MSE reductions versus baselines. Uncertainty is first‑class by sampling multiple outputs (source).

How Hermes 4 and RLM complement each other

Two trends, one toolbox:

• Transparent problem solving: use Hermes 4 for hybrid reasoning, explanations, and schema‑bound outputs.
• Structured prediction: use Google RLM for fast, accurate, text‑to‑text regression over system state.

Combined workflow example:

1. Hermes 4 drafts several candidate plans or scenarios (auditable chain‑of‑thought).
2. RLM scores each scenario’s predicted performance from telemetry snapshots.
3. A controller selects the best plan based on scores and uncertainty.

This pairing gives you both a thinking engine and a system oracle.

Getting started & resources

Hermes 4

• Open weights and model pages: Hugging Face (70B), OpenRouter (405B), project site.
• Hardware: 14B/70B run on high‑memory single GPUs or multi‑GPU boxes; 405B needs sharding and heavy VRAM (details).
• Prompts: terse mode = “Final answer only.” Transparent mode = wrap with <think> … </think>. Use JSON templates to enforce schemas (LM Studio).
• Finetuning: keep Atropos‑style checks in CI to avoid drift (verification video).

RLM

• Background and papers: Google Research blog, and publications at research.google/pubs.
• Serialization: choose a stable JSON/YAML schema, normalize units/time zones, include short histories where helpful.
• Minimal dataset: ~500 labeled I/O pairs to start; split by time and hold out a slice for testing.
• Training tips: track rank correlation and MSE; sample multiple outputs to estimate uncertainty; retrain as configs change.

Conclusion

Reasoning is not one thing. Sometimes you need a clear proof; sometimes you need a sharp forecast. Hermes 4 provides control, visibility, and open‑weight reasoning you can deploy and inspect. Google’s RLM offers compact, adaptable text‑to‑text regression for system performance.

One phrase to remember: combine transparent reasoning with text‑to‑text regression for system performance.

Hermes 4 LLM is ready for real work; pair it thoughtfully with RLM where numeric forecasts are required. See Hermes 4 on OpenRouter and RLM notes on the Google Research blog.

FAQ

What makes Hermes 4 different from the base Llama 3.1 405B parameter model?

Answer: Hermes 4 keeps the same backbone but applies focused post‑training for hybrid reasoning: chain‑of‑thought transparency, schema control, and disciplined stopping. It is tuned to switch between terse answers and detailed traces on demand. See the OpenRouter entry: openrouter.ai/nousresearch/hermes-4-405b.

How do I enable or disable chain‑of‑thought transparency?

Answer: Use prompts. For short outputs: “Final answer only.” For visible steps: wrap the explanation in <think> … </think>. You can also provide JSON schemas to force structure. See model notes on LM Studio.

Can I fine‑tune Hermes 4 on my domain data?

Answer: Yes. The open weights allow custom finetunes. Keep data in clear formats, include multiple valid solutions, and add verifiers in your training loop to protect structure and correctness. See the 70B card on Hugging Face and verification examples (video).

What hardware do I need to run Hermes 4?

Answer: 14B/70B can run on high‑memory single GPUs or multi‑GPU rigs. The 405B model typically needs multi‑GPU inference with significant VRAM and careful sharding. Plan capacity before production. See OpenRouter: openrouter.ai.

Does the “when to stop” training hurt accuracy?

Answer: The team reports major reductions in runaway generations while keeping accuracy within ~5–12% depending on the benchmark. Many teams accept a small accuracy dip for stable, schema‑complete outputs (source).

What is Google’s RLM in simple terms?

Answer: A small encoder‑decoder that reads structured text (e.g., JSON describing your system) and writes numbers (the predicted metric) as text. It treats regression as language modeling and is fast to fine‑tune. See Google Research.

How does RLM handle uncertainty?

Answer: Sample the model multiple times. The spread of predictions gives an uncertainty estimate you can use in planners, simulators, or risk‑aware schedulers (source).

When should I pick Hermes 4 vs. RLM?

Answer: Choose Hermes 4 for explainable problem solving, tutoring, coding, and drafting policies. Choose RLM when you need a precise metric prediction from telemetry with minimal feature engineering. Many practical systems combine both: Hermes drafts, RLM scores.

Where can I see benchmarks and docs?

Answer: Hermes 4 resources: OpenRouter, Hugging Face, and the project site hermes4.nousresearch.com. RLM background and case studies: Google Research blog and research.google/pubs.