Controlling Reasoning Effort in LLMs

Learning to reason with LLMs

Categories of Inference-Time Scaling for Improved LLM Reasoning

And an Overview of Recent Inference-Scaling Papers

LeAct: Learning to Reason from Expert Actions

arXiv:2607. 21856v1 Announce Type: new Abstract: Modern reasoning models depend on reasoning data, today sourced from human annotations or distilled from stronger LLMs.

Knowledge Knows, Verbalization Tells: Disentangling Latent Directions for Mathematical Solvability in LLMs

Although LLMs have made significant progress in mathematical reasoning, determining whether a mathematical problem is solvable remains a fundamental yet challenging capability. While recent studies have probed internal representations of model solvability beliefs, verbalization has primarily been studied behaviorally rather than as an internal representation, limiting its analysis and manipulation.

Why Does Self-Distillation (Sometimes) Degrade the Reasoning Capability of LLMs?

arXiv:2603. 24472v4 Announce Type: replace-cross Abstract: Self-distillation has emerged as an effective post-training paradigm for LLMs, often improving performance while shortening reasoning traces.

Soft Guidance Starts to Outperform CoT Prompting as LLMs Improve

arXiv:2608. 03550v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting remains the standard baseline for evaluating models' reasoning abilities.

Towards Understanding On-Policy Distillation through the Lens of Test-Time Scaling

arXiv:2608. 11829v1 Announce Type: new Abstract: On-policy distillation (OPD) has emerged as a promising post-training technique for enhancing LLM reasoning.

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.

Knowledge Knows, Verbalization Tells: Disentangling Latent Directions for Mathematical Solvability in LLMs

arXiv:2607. 05013v1 Announce Type: cross Abstract: Although LLMs have made significant progress in mathematical reasoning, determining whether a mathematical problem is solvable remains a fundamental yet challenging capability.

Latent Reward Steering: An Adaptive Inference-Time Framework that Implicitly Promotes Cognitive Behaviors in Reasoning LLMs

arXiv:2606. 00726v1 Announce Type: new Abstract: Strong reasoning depends not only on model knowledge but also on how effectively cognitive behaviors are deployed during generation.

Amplified Does Not Mean Predictive: Reasoning Behaviors in Thinking Models

arXiv:2608. 13760v1 Announce Type: cross Abstract: Which reasoning behaviors are associated with correct answers in reasoning models, and does reasoning-oriented training amplify those behaviors?

Flout at Your Own Risk: LLMs Struggle with Pragmatic Cooperativity Under Epistemic Asymmetry

arXiv:2607. 11053v1 Announce Type: cross Abstract: Fruitful collaborations rely on cooperative communications, including of contextual cues to incorporate into reasoning.