# HybridSFT

HybridAgent lets a single agent move back and forth between continuous latent reasoning and text interaction within one generation, so part of its thinking leaves token-by-token decoding while tool use stays intact. Text agents previously had to write every step as tokens, while latent-reasoning models lacked native tool access and were hard to inspect. Across 11 benchmarks in mathematics, science, medicine, search and agentic reasoning at 4B to 14B scale, it reports a 10.4% average gain over frontier text and latent agents, with 35.5%–54.7% fewer tokens and 1.8–2.6 times faster inference.

HybridAgent lets a single agent move back and forth between continuous latent reasoning and text interaction within one generation, so part of its thinking leaves token-by-token decoding while tool use stays intact. Text agents previously had to write every step as tokens, while latent-reasoning models lacked native tool access and were hard to inspect. Across 11 benchmarks in mathematics, science, medicine, search and agentic reasoning at 4B to 14B scale, it reports a 10.4% average gain over frontier text and latent agents, with 35.5%–54.7% fewer tokens and 1.8–2.6 times faster inference.