New method TNT tackles reward hacking in reasoning models

By PulseAugur Editorial · [1 sources] · 2026-06-09 04:00

Researchers have developed a new method called Thinking-Based Non-Thinking (TNT) to address reward hacking in hybrid reasoning models. This approach aims to optimize computational efficiency by enabling models to decide when to engage in complex reasoning and when to provide a direct answer. TNT reportedly reduces token usage by approximately 50% while improving accuracy on mathematical benchmarks, achieving a better trade-off between performance and efficiency than existing methods. AI

IMPACT This method could lead to more efficient and accurate reasoning models, reducing computational costs for complex tasks.

RANK_REASON The cluster contains an academic paper detailing a new method for training AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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COVERAGE [1]

arXiv cs.AI TIER_1 English(EN) · Siyuan Gan, Jiaheng Liu, Boyan Wang, Tianpei Yang, Runqing Miao, Yuyao Zhang, Fanyu Meng, Junlan Feng, Linjian Meng, Jing Huo, Yang Gao · 2026-06-09 04:00

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. However, their performance mainly stems from thinking, a long Chain of Thought (CoT), which significantly increase computational o…

COVERAGE [1]

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

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