Scaling of Capability and Efficiency at Inference Time in Large Reasoning Models
cs.LG, physics.soc-ph
Submitted: 2026-09-22
Updated: 2026-09-24
Code: https://github.com/zohairshafi/capability-efficiency-scaling
Terminology
Sources
- Scaling Laws for Neural Language Models
- OpenAI o1 System Card
- Kimi k1.5: Scaling Reinforcement Learning with LLMs
- A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?
- Deep Learning Scaling is Predictable, Empirically
- Fine-Tuning Language Models from Human Preferences
- Reinforcement Learning from Human Feedback
- Training Verifiers to Solve Math Word Problems
- Tulu 3: Pushing Frontiers in Open Language Model Post-Training
- A Survey of Reinforcement Learning for Large Reasoning Models
- Does RLHF Scale? Exploring the Impacts From Data, Model, and Method
- Constitutional AI: Harmlessness from AI Feedback
- Between Underthinking and Overthinking: An Empirical Study of Reasoning Length and correctness in LLMs
- Understanding Performance Gap Between Parallel and Sequential Sampling in Large Reasoning Models
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks