OverThink
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Thinking Past the Answer: Evaluating Harmful Overthinking in Large Reasoning Models
Announce Type: new Abstract: Large Reasoning Models (LRMs) improve performance by generating explicit intermediate reasoning traces through increased test-time compute, yet the assumption that longer reasoning is consistently beneficial remains under-examined. While recent evidence shows that additional reasoning can lead models to overthink, we ask: "Once a model has reached the correct answer, does further reasoning refine the solution, or deviate from it?" To study the dynamics after...
Marcus Aurelius Had Anxiety Too – Stoicism for People Who Overthink
Marcus Aurelius Had Anxiety Too — Stoicism for People Who Overthink The Examined Life · Issue #4 Three issues in, we’ve covered your attention, your gut, and your ability to spot manipulation. This week we step back even further — into history, into philosophy, and into a set of ideas that have survived 2,000 years because they keep being true. This is the issue I most wanted to write.
DyCon: Dynamic Reasoning Control via Evolving Difficulty Modeling
arXiv:2606.07108v1 Announce Type: new Abstract: Recent advances in Large Reasoning Models (LRMs) demonstrate remarkable performance improvements by iteratively reflecting, exploring, and executing complex tasks, yet suffer from inefficiencies due to redundant reasoning, known as "overthinking". Existing methods to mitigate this issue either rely on static difficulty estimates or require task-specific training, and thus fail to adapt to the dynamic complexity during reasoning. In this work,...
DyCon: Dynamic Reasoning Control via Evolving Difficulty Modeling
arXiv:2606.07108v2 Announce Type: replace Abstract: Recent advances in Large Reasoning Models (LRMs) demonstrate remarkable performance improvements by iteratively reflecting, exploring, and executing complex tasks, yet suffer from inefficiencies due to redundant reasoning, known as "overthinking". Existing methods to mitigate this issue either rely on static difficulty estimates or require task-specific training, and thus fail to adapt to the dynamic complexity during reasoning. In this...
SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning
arXiv:2605.30832v1 Announce Type: new Abstract: Recent advances in Large Reasoning Models have significantly improved chain-of-thought (CoT) capabilities via reinforcement learning (RL). However, generated reasoning chains frequently suffer from structural redundancy (i.e., \emph{overthinking}), incurring high computational overhead without improving answer correctness. Existing mitigation strategies typically rely on token-uniform length penalties, which provide coarse, segment-agnostic...
What’s the secret to getting the perfect amount of sleep? Don’t worry about it! | Emma Beddington
Research out this month says you should aim for between 6.4 and 7.8 hours a night. But if you’re getting that granular about shut-eye, you’re overthinking itHow did you sleep last night? Did your smartring congratulate you on 8.5 sleepmaxxed hours in a cool, blackout-dark room after two hours’ withdrawal from blue light and “devices” and 480ml (per this month’s Vogue) of tart cherry juice?
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 overhead. To address this overthinking problem, existing work focuses on using reinforcement learning (RL) to train hybrid reasoning models that automatically decide whether to engage in thinking or not based on the...
Thinking Economically: A Hierarchical Framework for Adaptive-Complexity Reasoning in LLMs
Announce Type: new Abstract: Chain-of-Thought (CoT) has significantly enhanced LLM reasoning, yet often incurs substantial computational overhead due to "overthinking": generating excessively long rationales without commensurate accuracy gains. Existing efficiency methods typically apply uniform compression, which overlooks a critical observation that reasoning complexity is heterogeneous at two distinct granularity: across different problems and within individual reasoning steps. This...
SmartThinker: Progressive Chain-of-Thought Length Calibration for Efficient Large Language Model Reasoning
arXiv:2603.08000v2 Announce Type: replace Abstract: Large reasoning models (LRMs) like OpenAI o1 and DeepSeek-R1 achieve high accuracy on complex tasks by adopting long chain-of-thought (CoT) reasoning paths. However, the inherent verbosity of these processes frequently results in redundancy and overthinking. To address this issue, existing works leverage Group Relative Policy Optimization (GRPO) to reduce LRM output length, but their static length reward design cannot dynamically adapt...
RecurGuard: Runtime Monitoring for Reasoning-Token Consumption Attacks
Announce Type: new Abstract: Reasoning-capable large language models can be induced to spend their generation budget on injected decoy tasks rather than answering the user's question, causing denial of service when no final answer is produced and denial of wallet when excess output tokens are billed. Input-side safety classifiers often miss these attacks because the injected prompts can appear syntactically benign. We build RecurGuard, a runtime monitor for detecting reasoning-chain...