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ARXIV:2604.19087 · LLM CONTROL · SUBMITTED 22 APR · 02:13 UTC · FRESHNESS STALE
ARXIV:2604.19087LLM CONTROLSUBMITTED 22 APR · 02:13 UTCFRESHNESS STALEShashank Sharma · Janina Hoffmann · Vinay Namboodiri · arXiv
OLLM is a plug-in for LLMs that replaces single token prediction with a set of learned options, significantly improving controllability, robustness, and sample efficiency in tasks like math reasoning.
Opportunity summary
Pain OLLM is a plug-in for LLMs that replaces single token prediction with a set of learned options, significantly improving controllability, robustness, and sample efficiency in tasks like math reasoning.
Evidence 16 refs | 3 sources | 67% coverage
Blocker Evidence unverified
OLLM is a plug-in for LLMs that replaces single token prediction with a set of learned options, significantly improving controllability, robustness, and sample efficiency in tasks like math reasoning. Instead of relying on temperature…
We introduce Options LLM (OLLM), a simple, general method that replaces the single next-token prediction of standard LLMs with a \textit{set of learned options} for the next token, indexed by a discrete latent variable.…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Our results demonstrate that optionized next-token modeling enhances controllability, robustness, and efficiency in math reasoning, and highlight latent-space policy learning as a promising direction…
LLM Control moved forward this cycle; last verified April 2026. Public score 8.0/10. Production flags indicate code availability.
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OLLM is a plug-in for LLMs that replaces single token prediction with a set of learned options, significantly improving controllability, robustness, and sample efficiency in tasks like math reasoning.
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10.48550/arXiv.2604.19087OLLM is a plug-in for LLMs that replaces single token prediction with a set of learned options, significantly improving controllability, robustness, and sample efficiency in tasks like math reasoning.
Abstract
We introduce Options LLM (OLLM), a simple, general method that replaces the single next-token prediction of standard LLMs with a \textit{set of learned options} for the next token, indexed by a discrete latent variable. Instead of relying on temperature or sampling heuristics to induce diversity, OLLM models variation explicitly: a small latent space parametrizes multiple plausible next-token options which can be selected or searched by a downstream policy. Architecturally, OLLM is 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. We apply OLLM to a 1.7B-parameter backbone (only $1.56\%$ of parameters trainable) trained on OpenMathReasoning and evaluated on OmniMath. The SOTA LoRA-adapted baselines peak at $51\%$ final answer correctness, while OLLM's option set allows up to $\sim 70\%$ under optimal latent selection. We then train a compact policy in the latent space that emits latents to control generation. Operating in a low-dimensional option space makes reward optimization far more sample-efficient and substantially reduces common misalignments (e.g., language switching or degenerate reasoning), as the policy is constrained to options learned during SFT. Crucially, this alignment arises from model structure rather than additional KL or handcrafted alignment losses. Our results demonstrate that optionized next-token modeling enhances controllability, robustness, and efficiency in math reasoning, and highlight latent-space policy learning as a promising direction for reinforcement learning in LLMs.
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Proof status
unverified16 refs; 3 sources; 67% coverage.
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PROBLEM
OLLM is a plug-in for LLMs that replaces single token prediction with a set of learned options, significantly improving controllability, robustness, and sample efficiency in tasks like math reasoning. Instead of relying on temperature or sampling heuristics to induce diversity,...
METHOD
We introduce Options LLM (OLLM), a simple, general method that replaces the single next-token prediction of standard LLMs with a \textit{set of learned options} for the next token, indexed by a discrete latent variable. Instead of relying on temperature or sampling heuristics to...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Our results demonstrate that optionized next-token modeling enhances controllability, robustness, and efficiency in math reasoning, and highlight latent-space policy learning as a promising direction for...
WHY NOW
LLM Control moved forward this cycle; last verified April 2026. Public score 8.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 13, "author": "Shashank Sharma; Janina Hoffmann; Vinay Namboodiri", "title": "OLLM: Options-based Large Language Models", "creation date": null, "modification date": null
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OLLM is a plug-in for LLMs that replaces single token prediction with a set of learned options, significantly improving controllability, robustness, and sample efficiency in tasks like math reasoning.
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LLM Control
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Evidence coverage
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16 refs / 3 sources / 67% coverage
stale
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Build readiness
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Evidence
16 references, 3 sources, 67% evidence coverage.
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