Nous Research Team Releases Hermes 4: A Family of Open-Weight AI Models with Hybrid Reasoning
Nous Analysis has launched Hermes 4, a household of open-weight fashions (14B, 70B, and 405B parameter sizes based mostly on Llama 3.1 checkpoints) that achieves frontier-level efficiency via pure post-training strategies. Hermes 4 introduces hybrid reasoning – fashions can toggle between commonplace responses and specific reasoning utilizing <assume>...</assume> tags when complicated issues require deeper deliberation.
What makes Hermes 4 significantly vital is its achievement of state-of-the-art efficiency amongst open-weight fashions whereas sustaining full transparency and impartial alignment philosophy, demonstrating that subtle reasoning capabilities could be developed fully via open-source methodologies.
DataForge: Graph-Based mostly Artificial Information Era
DataForge is the primary element behind Hermes 4’s core construction. However what’s DataForge? DataForge is a revolutionary graph-based artificial information era system that transforms how coaching information is created. Not like conventional curation approaches, DataForge operates via a directed acyclic graph (DAG) the place every node implements a PDDL (Planning Area Definition Language) motion interface.
Every node specifies preconditions, postconditions, and transformations, facilitating the automated creation of complicated information pipelines. By utilizing pre-training seed information from DCLM and FineWeb, the system can remodel a Wikipedia article right into a rap music, after which generate instruction-answer pairs based mostly on that transformation.
This strategy generates roughly 5 million samples totaling 19 billion tokens, with reasoning samples being deliberately token-heavy – averaging 5 occasions extra tokens than non-reasoning counterparts to accommodate considering traces as much as 16,000 tokens lengthy.
Rejection Sampling at Unprecedented Scale
Hermes 4 makes use of Atropos, Nous Analysis’s open-source reinforcement studying setting, to implement rejection sampling throughout roughly 1,000 completely different task-specific verifiers. This huge verification infrastructure filters for high-quality reasoning trajectories throughout numerous domains.
Key verification environments embrace Reply Format Coaching (rewarding right formatting throughout 150+ output codecs), Instruction Following (utilizing RLVR-IFEval duties with complicated constraints), Schema Adherence (for JSON era utilizing Pydantic fashions), and Instrument Use coaching for agentic habits.
The rejection sampling course of creates a big corpus of verified reasoning trajectories, with a number of distinctive resolution paths to the identical verified consequence. This strategy ensures the mannequin learns strong reasoning patterns fairly than memorizing particular resolution templates.
Size Management: Fixing Overlong Era
Considered one of Hermes 4’s most modern contributions addresses the overlong reasoning downside – the place reasoning fashions generate excessively lengthy chains of thought with out termination. The analysis staff found their 14B mannequin reached most context size 60% of the time on LiveCodeBench when in reasoning mode.
Their tremendous efficient resolution includes a second supervised fine-tuning stage educating fashions to cease reasoning at precisely 30,000 tokens:
- Generate reasoning traces from the present coverage
- Insert
</assume>tokens at precisely 30,000 tokens - Prepare solely on the termination choice, not the reasoning chain
- Apply gradient updates solely to
</assume>and<eos>tokens
This strategy achieves outstanding outcomes: 78.4% discount in overlong era on AIME’24, 65.3% on AIME’25, and 79.8% on LiveCodeBench, with solely 4.7% to 12.7% relative accuracy value. By focusing studying indicators fully on the termination choice, the strategy avoids mannequin collapse dangers whereas educating efficient “counting habits.”
Benchmark Efficiency and Impartial Alignment
Hermes 4 demonstrates state-of-the-art efficiency amongst open-weight fashions. The 405B mannequin achieves 96.3% on MATH-500 (reasoning mode), 81.9% on AIME’24, 78.1% on AIME’25, 70.5% on GPQA Diamond, and 61.3% on LiveCodeBench.
Significantly notable is its efficiency on RefusalBench, reaching 57.1% in reasoning mode – the very best rating amongst evaluated fashions, considerably outperforming GPT-4o (17.67%) and Claude Sonnet 4 (17%). This demonstrates the mannequin’s willingness to have interaction with controversial matters whereas sustaining acceptable boundaries, reflecting Nous Analysis’s impartial alignment philosophy.
Technical Structure and Coaching
Hermes 4 coaching leverages a modified TorchTitan throughout 192 NVIDIA B200 GPUs. The system handles extremely heterogeneous pattern size distribution via environment friendly packing (reaching >99.9% batch effectivity), flex consideration, and complex loss masking the place solely assistant-role tokens contribute to cross-entropy loss.
Coaching follows a cosine studying price schedule with 300 warmup steps and 9,000 whole steps at 16,384 token context size with world batch measurement of 384 samples, combining Information Parallelism, Tensor Parallelism, and Totally Sharded Information Parallelism.
Abstract
Hermes 4 marks a big development in open-source AI growth, proving that frontier-level reasoning capabilities could be achieved via clear, reproducible methodologies with out counting on proprietary coaching information or closed growth processes. By combining modern graph-based artificial information era, massive-scale rejection sampling, and stylish size management mechanisms, Nous Analysis has created fashions that not solely match the efficiency of main proprietary techniques but in addition keep the impartial alignment and steerability that make them genuinely helpful instruments fairly than restrictive assistants
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