HybridAgent thinks in latent space, acts in text: +10.4% accuracy, up to 3.7x cheaper
AccBalanced · x · 2026-10-15
A Stanford/Together AI paper introduces Hybrid Reasoning Agents: a lightweight HybridLink adapter feeds the model's hidden states back as input so agents can reason in latent space and switch to text only for tool calls and key decisions, with silent steps still decodable. With HybridSFT/HybridRL post-training and multi-turn memory, any pretrained LLM can become a HybridAgent. Across 11 benchmarks and 3 model scales it improves performance by 10.4% while cutting tokens 35.5–54.7%, speeding inference 1.8–2.6x and reducing costs 2.7–3.7x; the 14B model scores 52.4 on GAIA vs 43.7 for a text-only ARPO baseline, 2.1x faster. Code is open-sourced.
Related event: Stanford and Together AI Introduce HybridAgent That Thinks in Latent Space(5 posts)→
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