I recently tested LFM2-2.6B-Exp, an experimental language model developed by Liquid AI, to see how well it handles differential equations in a practical, step-by-step setting. The goal was simple: move beyond benchmarks and check how the model performs when asked to actually reason through math problems the way students, engineers, or researchers would use it.

About the model and the company behind it LFM2-2.6B-Exp is an experimental checkpoint built on Liquid AI’s LFM2 architecture. What makes it notable is its training approach: it relies on pure reinforcement learning, with no supervised fine-tuning warm-up and no distillation from larger teacher models. The training was done sequentially, starting with instruction following and later expanding into knowledge, math, and limited tool use. Liquid AI itself is worth highlighting. The company is highly focused on edge AI, designing models meant to be efficient, compact, and deployable outside large data-center environments. They have released a family of models spanning from very small footprints (on the order of a few million parameters) up through multi-billion-parameter models, reaching into the ~8B range. LFM2-2.6B-Exp sits squarely in the middle of that spectrum, aiming to balance capability with efficiency.

What worked well in differential equations In testing LFM2-2.6B-Exp on differential equations, the model performed impressively for its size on standard material: First-order ODEs (separable and linear) Second-order linear ODEs with constant coefficients Nonhomogeneous equations using undetermined coefficients Initial value problems with well-defined solution procedures

In these cases, the model followed instructions closely, laid out multi-step solutions clearly, and usually arrived at correct results. For a 2.6B-parameter model, this level of procedural math competence is strong and often comparable to much larger models.

Where it struggled As the problems moved into more subtle territory, limitations became clear: Laplace transforms involving Heaviside (step) functions and time shifting Variation of parameters for nontrivial forcing terms Situations where maintaining symbolic invariants mattered more than following a familiar solution template

Here, the model sometimes produced answers that looked structurally correct but failed under careful verification. These were not token-limit issues; they were conceptual slips. This behavior is consistent with an RL-first training regime: the model has learned to produce well-formed, expected answers, but it does not always fully internalize the deeper mathematical invariants behind them.

A fair takeaway Based on this testing, I would characterize LFM2-2.6B-Exp as: Strong at instruction following and procedural math Reliable for standard undergraduate-level differential equations Less reliable for more theoretical or invariant-heavy methods

That assessment aligns closely with how Liquid AI describes the model themselves: an experimental research artifact, not a fully polished production system.

Why this matters What makes LFM2-2.6B-Exp interesting is not perfection, but direction. It shows how far reinforcement learning alone can push a relatively small model, especially when combined with a design philosophy aimed at efficiency and edge deployment. As Liquid AI continues to refine this approach, models like this offer a compelling glimpse into what capable, compact AI systems might look like in real-world, resource-constrained settings. For anyone interested in edge AI, alternative training strategies, or the practical limits of small-to-mid-sized language models, LFM2-2.6B-Exp is a model worth paying attention to.