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Unlike its predecessor, LFM2.5-8B-A1B is a reasoning-only model, producing an explicit chain of thought before its final answer. We adopted this strategy because MoE models generally run in compute-bound settings, where a smaller number of active parameters makes each reasoning token cheap. This provides a significant quality boost without compromising speed.

Thanks to reasoning and scaled-up training, this new version performs significantly better:

Training highlights

Tokenizer expansion. LFM2-8B-A1B was originally trained with a 65K BPE tokenizer optimized for our initial language coverage. To better support non-Latin scripts in LFM2.5, we doubled the vocabulary to 128K by extending the existing tokenizer in place rather than retraining the model from scratch.. We continued BPE merge training from the original merges on a multilingual corpus, which keeps most existing token IDs as identity mappings and makes every new token decompose deterministically into a sequence of original sub-tokens. We initialize the new embedding rows as the mean of their sub-token decompositions and copy the shared rows unchanged. We then recover quality through a brief two-stage adaptation: embedding-only training, followed by full-model continued pretraining.

The table below reports chars/token, roughly how much text each token carries: higher is better, and the new tokenizer is more efficient in all 16 languages

Context extension. We first extended the context window to 32K through a 2T token midtraining phase focused on reasoning, math, tool-use, and longer documents. We then extended the context to 128K by increasing the RoPE base θ and running an additional 400B token midtraining stage focused on long-document and long-trajectory data.

Doom loops. We added a targeted preference optimization stage to reduce doom loops in long reasoning traces. This stage identifies tokens that tend to trigger looping behavior in specific contexts, then redistributes probability mass toward plausible alternatives, while leaving the rest of the next-token distribution largely intact. During RL, we also added a lightweight shaping reward that discourages excessive use of common loop-inducing restart words like “Wait…”. We'll share more details on the full pipeline, objective, and empirical results in a dedicated blog post.

Hallucinations. Because of their small number of parameters, edge models have a limited knowledge capacity, which leads to more hallucinations. To mitigate hallucinations, we added a targeted RL stage that uses an avg@k-based reward over a diverse knowledge dataset. The goal is to reinforce abstention on queries beyond reliable knowledge while preserving existing knowledge. This produces a sharper knowledge boundary and clearer expression of uncertainty.

Benchmarks

We evaluated LFM2.5-8B-A1B across benchmarks covering knowledge, instruction following, math, and agentic workflows. The model is competitive with both dense alternatives with a similar total number of parameters and much larger MoEs.

The avg@k-based reward enables LFM2.5-8B-A1B to achieve a significantly lower hallucination rate while maintaining reasonable accuracy. It also leads on instruction following benchmarks, matching bigger MoEs like Gemma 4-26B at a fraction of the active parameter count.

Math and agentic workflows

On agentic benchmarks, LFM2.5-8B-A1B is competitive with bigger models and particularly strong on Tau2-Telecom. As agentic harnesses are becoming the main way to consume models, LFM2.5-8B-A1B is a first step towards powering on-device, fully private agents.

Sparse Inference, Everywhere

LFM2.5-8B-A1B ships with day-one support across the inference ecosystem:

• LEAP — Liquid's Edge AI Platform for iOS and Android deployment
• llama.cpp — GGUF checkpoints for efficient edge inference
• MLX — Optimized inference for Apple Silicon
• vLLM — GPU-accelerated serving for production throughput
• SGLang — GPU-accelerated serving for production throughput
• ONNX — Cross-platform inference across diverse accelerators

CPU inference. LFM2.5-8B-A1B ships with day-one llama.cpp support and runs on everyday consumer hardware.

@article{liquidAI20268BA1B,
  author  = {Liquid AI},
  title   = {LFM2.5-8B-A1B: Personal Assistant On Your Laptop},
  journal = {Liquid AI Blog},
  year    = {2026},
  note    = {https://www.liquid.ai/blog/lfm2-5-8b-a1b},
}