Source record

Published by Hugging Face Blog (T1.5 source). The original is at https://huggingface.co/blog/LiquidAI/lfm2-5-2-6b.

Pipeline notes

The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.

Liquid AI's LFM2.5-2.6B is pre-trained on ~34T tokens with a 128K context window, then post-trained into an agent through four stages. The Agentic RL pipeline separates model optimization, inference, and environment execution into a Training Engine, a Rollout Engine, and an orchestrating RL framework.

Lays out a concrete pipeline architecture — Training Engine, Rollout Engine, RL orchestrator — for turning a small open model into a local agent, useful for readers building or evaluating on-device agent stacks.

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