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ARXIV:2604.12229 · LLM REASONING · SUBMITTED 15 APR · 17:00 UTC · FRESHNESS STALE
ARXIV:2604.12229LLM REASONINGSUBMITTED 15 APR · 17:00 UTCFRESHNESS STALEJawad Hossain · Xiangyu Guo · Jiawei Zhou · Chong Liu · arXiv
A hint-assisted reasoning framework that uses cooperative small language models to improve mathematical problem-solving.
Opportunity summary
Pain A hint-assisted reasoning framework that uses cooperative small language models to improve mathematical problem-solving.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A hint-assisted reasoning framework that uses cooperative small language models to improve mathematical problem-solving. We address this challenge by introducing a hint-assisted reasoning framework that incrementally guides SLMs through multi-step mathematical problem solving.
Small language models (SLMs) often struggle with complex mathematical reasoning due to limited capacity to maintain long chains of intermediate steps and to recover from early errors. We address this challenge by introducing a…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. While the hint-generating SLM alone is not capable of solving the problems, its collaboration with a reasoning SLM enables effective guidance, forming a cooperative…
LLM Reasoning moved forward this cycle; last verified April 2026. Public score 8.0/10. Production flags indicate code availability.
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A hint-assisted reasoning framework that uses cooperative small language models to improve mathematical problem-solving.
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10.48550/arXiv.2604.12229A hint-assisted reasoning framework that uses cooperative small language models to improve mathematical problem-solving.
Abstract
Small language models (SLMs) often struggle with complex mathematical reasoning due to limited capacity to maintain long chains of intermediate steps and to recover from early errors. We address this challenge by introducing a hint-assisted reasoning framework that incrementally guides SLMs through multi-step mathematical problem solving. Our approach decomposes solutions into sequential reasoning steps and provides context-aware hints, where hints are generated by a separate SLM trained via distillation from a strong large language model. While the hint-generating SLM alone is not capable of solving the problems, its collaboration with a reasoning SLM enables effective guidance, forming a cooperative two-model system for reasoning. Each hint is generated conditionally on the problem statement and the accumulated reasoning history, providing stepwise, localized guidance without revealing full solutions. This reduces error propagation and allows the reasoning model to focus on manageable subproblems. Experiments across diverse mathematical benchmarks and models demonstrate that hint assistance consistently improves reasoning accuracy for SLMs, yielding substantial gains over standard prompting while preserving model efficiency. These results highlight that structured collaboration between SLMs-via hint generation and reasoning-offers an effective and lightweight mechanism for enhancing mathematical reasoning.
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PROBLEM
A hint-assisted reasoning framework that uses cooperative small language models to improve mathematical problem-solving. We address this challenge by introducing a hint-assisted reasoning framework that incrementally guides SLMs through multi-step mathematical problem solving.
METHOD
Small language models (SLMs) often struggle with complex mathematical reasoning due to limited capacity to maintain long chains of intermediate steps and to recover from early errors. We address this challenge by introducing a hint-assisted reasoning framework that incrementally...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. While the hint-generating SLM alone is not capable of solving the problems, its collaboration with a reasoning SLM enables effective guidance, forming a cooperative two-model system for reasoning. Code av...
WHY NOW
LLM Reasoning moved forward this cycle; last verified April 2026. Public score 8.0/10. Production flags indicate code availability.
Language Models Jawad Hossain University at Albany jhossain2@albany.edu Xiangyu Guo University at Buffalo xiangyug@buffalo.edu Jiawei Zhou† Stony Brook University jiawei.zhou.1@stonybrook
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rived from the oracle hint generation process: the problem statement P, the intermediate reasoning state Wt extracted from the ground-truth solution trajectory, and the corresponding oracle-generated hint ht
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solver to follow a more structured and reliable rea- soning trajectory, often matching the performance of LLM-guided reasoning while maintaining sig- nificantly lower token cost. In contrast, unguided reasoning (i.e.
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full solutions, GPT-5.2 was conditioned on the problem statement and the final answer to generate instructional hints. The average number of hints for AIME-2025 is 14. 4
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2025). In addition, we evaluateFT DeepSeek-R1- Distill-Qwen-7B, a fine-tuned SLM hint generator, to examine whether it can directly solve problems beyond its primary role of producing instructional hints
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Qwen2.5-Math-7B-Instruct 75.68% (56/74)85.14%(63/74) 79.73% (59/74) 54.05% (40/74) DeepSeek-R1-Distill-Qwen-7B 64.86% (48/74) 82.43% (61/74)85.14%(63/74)68.92%(51/74) DeepSeek-R1-Distill-Llama-8B 52.70% (39/74) 71
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The Phi-4-mini-reasoning (Xu et al., 2025) is an- other compact 3.8B-model specifically optimized for advanced mathematical reasoning and step-by- step problem-solving. Qwen2.5-Math (Yang et al.
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partial
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A hint-assisted reasoning framework that uses cooperative small language models to improve mathematical problem-solving.
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