arXiv AI
Aug 3
On the Generalization of Steering Vectors for Chain-of-Thought Faithfulness
arXiv:2607. 29062v1 Announce Type: new Abstract: Model capabilities have improved in large part due to scaling chain of thought.
Length Penalties Make Chain-of-Thought Less Monitorable
arXiv:2607. 09786v1 Announce Type: new Abstract: Length-penalized reinforcement learning can shorten chain-of-thought reasoning while hiding an influence that drives the model's answer.
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arXiv AI
Sep 1
Drop the Act: Probe-Filtered RL for Faithful Chain-of-Thought Reasoning
arXiv:2605.11467v2 Announce Type: replace-cross Abstract: Reasoning models post-hoc rationalize answers they have already committed to internally, producing chains of *reasoning theater*: deliberativ...
arXiv AI
Sep 25
To Think or Not to Think: Allocating Reasoning Where It Helps
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.
arXiv AI
Jun 30
Does Verbose Chain-of-Thought Really Help? In-Distribution Evidence that Content, Not Length, Matters
arXiv:2606. 30128v1 Announce Type: new Abstract: Chain-of-thought (CoT) prompting improves LLM reasoning, but the source is contested: do the intermediate steps help because they carry useful semantic content, or because conditioning on more tokens buys extra computation before the model commits to an answer?
arXiv AI
3d ago
A helps B while B hurts A: directed transfer in instruction-tuning mixture
arXiv:2609.39702v1 Announce Type: new Abstract: Adapting a language model to a specialized corpus means choosing which instruction-tuning tasks to train on under a fixed budget, and testing one choic...
arXiv AI
Aug 5
Shorter Reasoning, Earlier Answers? An Evaluation of Reasoning Interfaces
arXiv:2608. 03401v1 Announce Type: cross Abstract: Large language models often reason at length before answering, increasing cost and latency.
arXiv AI
Aug 11
Understanding and Mitigating Premature Confidence for Better LLM Reasoning
arXiv:2605. 24396v2 Announce Type: replace Abstract: Long chains of thought (CoT) from current language models frequently contain logical gaps and unjustified leaps, limiting the gains from additional test-time compute.
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.
arXiv AI
Jul 14
Valid $\ne$ Necessary: Diagnosing Latent Inefficiency in Chain-of-Thought
arXiv:2607. 11266v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting has significantly advanced the reasoning capabilities of Large Language Models (LLMs), yet it often incurs substantial computational costs due to over-reasoning: the generation of redundant, verbose, or irrelevant steps.
arXiv Computer Vision
Aug 25
VIG: Visual Information Gain as a Reward Signal for Multimodal Chain-of-Thought Compression
The paper introduces VIG (Visual Information Gain), an information‑theoretic reward that evaluates each token in a multimodal chain‑of‑thought by measuring how much the image reduces its predictive uncertainty. VIG is computed online using two forward passes—one with and one without the image—eliminating the need for reference chains or external annotations. Experiments on six multimodal reasoning benchmarks and multiple Qwen3‑VL‑Thinking model sizes show that VIG consistently improves the accuracy–efficiency trade‑off, demonstrating that efficient multimodal reasoning arises from increasing visual information density rather than merely limiting chain length.
arXiv AI
Aug 21
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.
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.