Papers with Math500
Chain-in-Tree: Back to Sequential Reasoning in LLM Tree Search (2026.findings-acl)
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| Challenge: | Large language models excel at tasks such as mathematical and commonsense reasoning, but their performance improves further when additional test-time compute is allocated. |
| Approach: | They propose a plug-in framework that decides when to branch during search instead of expanding at every step. |
| Outcome: | The proposed framework reduces token generation, model calls, and runtime by 75-85% on GSM8K and Math500, with negligible or no accuracy loss. |
Offloaded Reasoning: Efficient Inference for Large Language Models via Modular Reasoning and Refinement (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) demonstrate strong reasoning capabilities but are expensive to run at inference time, limiting their practical deployment. |
| Approach: | They propose Offloaded Reasoning, a modular strategy where a lightweight model generates intermediate reasoning traces that are then used by a larger model to produce the final answer. |
| Outcome: | The proposed approach achieves faster inferences than full large-model reasoning with minimal accuracy loss while recovering or exceeding full accuracy at substantially lower cost. |
PIAST: Rapid Prompting with In-context Augmentation for Scarce Training data (2026.acl-long)
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| Challenge: | Existing prompt generation methods are impractical in time and data constrained settings. |
| Approach: | They propose a fast automatic prompt construction algorithm that augments human instructions by generating a small set of few shot examples. |
| Outcome: | The proposed method outperforms existing prompting methods on classification, simplification, and MedQA. |
d-TreeRPO: Towards More Reliable Policy Optimization for Diffusion Language Models (2026.acl-long)
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Leyi Pan, Shuchang Tao, Yunpeng Zhai, Zheyu Fu, Liancheng Fang, Minghua He, Lingzhe Zhang, Zhaoyang Liu, Bolin Ding, Aiwei Liu, Lijie Wen
| Challenge: | Existing RL methods suffer from reliability bottlenecks due to reward sparsity and intractable computations . d-TreeRPO provides fine-grained and verifiable step-wise reward signals . |
| Approach: | They propose a reliable reinforcement learning framework for diffusion large language models that leverages tree-structured rollouts and bottom-up advantage computation based on verifiable outcome rewards. |
| Outcome: | The proposed framework outperforms baseline models and achieves significant improvements across reasoning benchmarks. |
Reinforcement Learning for Diffusion LLMs via Energy-Based Gibbs Alignment (2026.acl-long)
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| Challenge: | Diffusion Large Language Models (dLLMs) offer parallel decoding and bidirectional context modeling . aligning dLLms with reinforcement learning (RL) remains a challenge . |
| Approach: | They propose a variational framework that reformulates RL for dLLMs as a distribution matching problem. |
| Outcome: | The proposed framework reformulates RL for dLLMs as a distribution matching problem. |