New OLLM Architecture Achieves 70% Accuracy on Math Reasoning Benchmark

Researchers introduce Options-based LLM, a method that uses learned token options to improve controllability and efficiency in mathematical reasoning tasks.

New OLLM Architecture Achieves 70% Accuracy on Math Reasoning Benchmark

Researchers have introduced Options LLM (OLLM), a novel method that modifies how large language models predict the next token during text generation, according to a paper published on arxiv.org on April 22, 2026.

According to the paper, OLLM replaces standard single next-token prediction with “a set of learned options for the next token, indexed by a discrete latent variable.” The architecture functions as a “lightweight ‘plug-in’ that inserts two layers: an encoder and a decoder, before the output head, allowing almost any pretrained LLM to be converted with minimal additional parameters.”

The researchers applied OLLM to a 1.7B-parameter model with only 1.56% of parameters trainable, training it on OpenMathReasoning and evaluating on OmniMath. According to the paper, “The SOTA LoRA-adapted baselines peak at 51% final answer correctness, while OLLM’s option set allows up to ~70% under optimal latent selection.”

The method also includes training “a compact policy in the latent space that emits latents to control generation.” According to the researchers, operating in this low-dimensional option space “makes reward optimization far more sample-efficient and substantially reduces common misalignments (e.g., language switching or degenerate reasoning).” The paper notes that “this alignment arises from model structure rather than additional KL or handcrafted alignment losses.”

OLLMLLMmathematical reasoningalignmentarxiv