arXiv AI August 21, 2026 By Gijs Kassenaar, Zhao Yang, Vincent Fran\c{c}ois-Lavet

Learning When to Think: Adaptive Reasoning for Test-Time Compute Allocation

arXiv:2608. 20256v1 Announce Type: new Abstract: Reasoning language models trained with reinforcement learning typically operate under a fixed token budget rather than an explicitly adaptive one, which can lead to over-computation on easy problems and insufficient computation on difficult ones.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 9

The Easy, the Hard, and the Learnable: Confidence and Difficulty-Adaptive Policy Optimization for LLM Reasoning

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

llms reinforcement-learning benchmarks

arXiv AI

Aug 18

Measuring Reward Hacking and Reasoning-Answer Decoupling Under Position-Confounded Optimization

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

llms benchmarks

arXiv AI

Jun 9

Thinking-Based Non-Thinking: Solving the Reward Hacking Problem in Training Hybrid Reasoning Models via Reinforcement Learning

arXiv:2601. 04805v2 Announce Type: replace Abstract: Large reasoning models (LRMs) have attracted much attention due to their exceptional performance.

By Siyuan Gan, Jiaheng Liu, Boyan Wang, Tianpei Yang, Runqing Miao, Yuyao Zhang, Fanyu Meng, Junlan Feng, Linjian Meng, Jing Huo, Yang Gao

reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jul 27

Re-FORC: Adaptive Reward Prediction for Efficient Chain-of-Thought Reasoning

arXiv:2511. 02130v2 Announce Type: replace-cross Abstract: We propose Re-FORC, an adaptive reward prediction method that, given a query, enables prediction of the expected future rewards as a function of the number of future thinking tokens.

By Renos Zabounidis, Aditya Golatkar, Michael Kleinman, Alessandro Achille, Wei Xia, Stefano Soatto

arXiv AI

Aug 6

Fewer Tokens, Smaller Cache: Reward-Coordinated Efficient Reasoning

arXiv:2608. 04771v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) excel on complex tasks through long chain-of-thought (CoT) reasoning, but their lengthy intermediate steps cause severe overthinking that inflates inference cost.

By Qiyuan Zhu, Dezhi Li, Pengyu Cheng, Tianle Chen, Jiacheng Wang, Ruijie Shen, Hao Gu, Sida Lin, Zirui Liu, Jiacheng Liu, Sirui Han

benchmarks

arXiv AI

Aug 28

The Reasoning Tax: Token Economics of LLM Reasoning Across Task Types and Deployment Contexts

The paper introduces the Token Economy Score (TES), a metric that quantifies the accuracy gain of reasoning-capable large language models relative to non-reasoning baselines, normalized by token generation cost. An empirical study across 151 runs on seven diverse benchmarks shows that task structure—such as sequential inference chains—predicts higher TES, while knowledge-recall tasks yield lower TES despite difficulty. The analysis also reveals diminishing returns at higher reasoning effort and highlights how deployment context, via Reasoning Cost Share and Deployment Cost Multiplier, can alter the economic viability of reasoning workloads.

By Sachin Gopal Wani, Ajay Dholakia, David Ellison

llms benchmarks

One email a morning, machine-written

Related stories

The Easy, the Hard, and the Learnable: Confidence and Difficulty-Adaptive Policy Optimization for LLM Reasoning

Measuring Reward Hacking and Reasoning-Answer Decoupling Under Position-Confounded Optimization

Thinking-Based Non-Thinking: Solving the Reward Hacking Problem in Training Hybrid Reasoning Models via Reinforcement Learning

Re-FORC: Adaptive Reward Prediction for Efficient Chain-of-Thought Reasoning

Fewer Tokens, Smaller Cache: Reward-Coordinated Efficient Reasoning

The Reasoning Tax: Token Economics of LLM Reasoning Across Task Types and Deployment Contexts