The paper introduces CARE, a contrastive accuracy reward estimation method that adaptively adjusts reasoning length for large language models. By comparing beneficial length adjustments from online sampled responses, CARE applies adaptive length rewards within Group Relative Policy Optimization without extra hyperparameters or inference cost. Experiments on multiple reasoning benchmarks show that CARE improves Pass@1 by up to 4% while reducing reasoning length by 37%, achieving higher token efficiency.
By Zhengdong He, Yunfan Zhou, Jianguo Yao, Haibing Guan, Xijun Li
The paper introduces Budget‑Efficient Thinking (BET), a two‑stage framework that treats adaptive reasoning as a computational investment, aligning solve‑or‑fold decisions with expected return rather than perceived difficulty. BET learns three distinct behaviors: concise short solves for easy queries, early abstention (nice fold) when further reasoning is unlikely to pay off, and allocating sufficient compute (hero call) for hard‑but‑solvable questions. Experiments on seven benchmarks with three base models show BET cuts reasoning tokens by 54% while boosting accuracy by up to 3.2%, and it transfers effectively to scientific QA and logical reasoning tasks.
By Zhaomeng Zhou, Lan Zhang, Junyang Wang, Mu Yuan, Songlin Liu, Tingzhao Li, Yiqing Hu, Yumeng Zhao
When2Think introduces a post‑training framework that dynamically allocates reasoning depth in Large Reasoning Models based on instance difficulty. The method uses Instance‑level Difficulty‑Aware Control (IDAC) to shape rewards with pre‑computed accuracy and token usage statistics, enabling stable, critic‑free optimization without learned reward models. Experiments on mathematical benchmarks show that When2Think improves accuracy‑efficiency trade‑offs, achieving higher Pass@3 scores while reducing token usage compared to baseline models.
By Jaejun Shim, HyunJin Kim, Young Jin Kim, JinYeong Bak
arXiv:2510.01581v2 Announce Type: replace-cross
Abstract: Recent thinking models are capable of solving complex reasoning tasks by scaling test-time compute, but this scaling should be allocated in l...
By Joykirat Singh, Justin Chih-Yao Chen, Archiki Prasad, Elias Stengel-Eskin, Akshay Nambi, Mohit Bansal
arXiv:2606. 07950v1 Announce Type: new Abstract: RL with verifiable rewards can substantially improve LLM reasoning, yet standard GRPO-style training often treats easy, hard, and learnable questions alike through uniform sampling and weighting, leading to inefficient compute allocation.
By Zhanke Zhou, Xiangyu Lu, Chentao Cao, Brando Miranda, Tongliang Liu, Bo Han, Sanmi Koyejo
The paper introduces PORA, a method that teaches large language model agents to decide when to allocate more reasoning steps versus fewer, based on the expected downstream benefit. PORA trains by running each state twice—once with short reasoning and once with long reasoning—and learns to predict the value of extended reasoning. During inference, a controller uses this prediction and remaining budget information to choose the appropriate reasoning length without running both options, improving performance across diverse tasks.
By Bo Yan, Qi Zhang, Zhen Tan, Tianlong Chen, Song Wang
arXiv:2608. 15445v1 Announce Type: new Abstract: When a reward is correct on every training example yet consistent with more than one goal, a model can acquire an unintended one, a failure known as goal misgeneralization.
By Suyash Maniyar, Armaan Sandhu, Abhishek Mishra
arXiv:2510.27042v3 Announce Type: replace
Abstract: Increasing the thinking budget of AI models can significantly improve accuracy, but not all questions warrant the same amount of reasoning. Users m...
By Michael Kleinman, Matthew Trager, Alessandro Achille, Wei Xia, Stefano Soatto
arXiv:2610.09804v1 Announce Type: new
Abstract: Reinforcement learning is now central to eliciting reasoning in large language models, while in the popular algorithm Group Relative Policy Optimizatio...
By Yingxiang Yang, Weihang Xiao, Zhunxuan Wang, Joshua Flashner, Niresh Agarwal
arXiv:2610.25768v2 Announce Type: replace
Abstract: Large language models (LLMs) often generate more reasoning tokens than needed. Limiting the reasoning budget reduces cost, but forcing an answer fr...
By Zhongyin Wang, Zhaoqian Xue, Junting Mu, Haozheng Liu, Ye Yuan, Haolun Wu, Lingyao Li
The paper introduces AnytimeReasoner, a framework that optimizes large language models for anytime reasoning by truncating the reasoning process to fit sampled token budgets and requiring the model to summarize the answer for verification. This approach adds verifiable dense rewards, improving credit assignment in reinforcement learning. The authors also propose Budget Relative Policy Optimization (BRPO), a variance reduction technique that enhances learning robustness and efficiency, and demonstrate that their method outperforms GRPO on mathematical reasoning tasks across various budgets and prior distributions.
By Penghui Qi, Zichen Liu, Tianyu Pang, Chao Du, Wee Sun Lee, Min Lin
The paper introduces CLOSEDLOOPGAUSSIAN, an adaptive curriculum strategy for Group Relative Policy Optimization (GRPO) that dynamically adjusts the pace and spread of an Easy-to-Hard Gaussian sampler based on probe estimates of per-difficulty accuracy. Unlike fixed easy-to-hard schedules, the method accelerates training when the model outpaces a reference curriculum, slows it down when it lags, and broadens sampling when earlier difficulties regress, thereby keeping training focused on learnable yet challenging tasks. Experiments on symbolic arithmetic reasoning demonstrate that CLOSEDLOOPGAUSSIAN outperforms vanilla GRPO and fixed open-loop curricula, especially in intermediate-difficulty regimes where group-relative learning signals are most informative.
By Yuv Raj Pant, Moshiur Rahman Tonmoy, Kaiqun Fu