Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data

2026-04-20

AI Summary

Large language models have reached performance saturation on standard reasoning benchmarks, leading to vanishing advantage signals and policy collapse in reinforcement learning. To address this, this work proposes the Mixed-CUTS training framework, which introduces a parameter-free Constrained Uniform Top-K Sampling (CUTS) strategy. CUTS performs structure-preserving uniform sampling within high-confidence candidate sets and blends it with conventional sampling to generate reasoning trajectories that balance exploration and exploitation. Coupled with a group-relative advantage estimation, the method effectively preserves solution diversity within the semantic manifold and substantially mitigates policy degradation. Experiments on the Qwen3 model demonstrate that Mixed-CUTS improves Pass@1 accuracy by 15.1% over standard GRPO on the AIME25 out-of-domain generalization benchmark.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Search and Optimization: Learning to SearchPlanning, Routing, and Scheduling: Planning with Language Models

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsEconomics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Reinforcement Learning (RL) enhances LLM reasoning, yet a paradox emerges as models scale: strong base models saturate standard benchmarks (e.g., MATH), yielding correct but homogeneous solutions. In such environments, the lack of failure cases causes the advantage signal in group-relative algorithms (e.g., GRPO) to vanish, driving policies into mode collapse. To address this, we propose Constrained Uniform Top-K Sampling (CUTS), a parameter-free decoding strategy enforcing structure-preserving exploration. Unlike standard sampling that follows model biases, CUTS flattens the local optimization landscape by sampling uniformly from constrained high-confidence candidates. We integrate this into Mixed-CUTS, a training framework synergizing exploitative and exploratory rollouts to amplify intra-group advantage variance. Experiments on Qwen3 models demonstrate that our approach prevents policy degeneration and significantly boosts out-of-domain generalization. Notably, Mixed-CUTS improves Pass@1 accuracy on the challenging AIME25 benchmark by up to 15.1% over standard GRPO, validating that maintaining diversity within the semantic manifold is critical for rigorous reasoning.

Problem

• Reinforcement Learning
• Mode Collapse
• Reasoning Saturation
• Advantage Signal
• Policy Degeneration

Innovation

• Reinforcement Learning
• Reasoning Diversity
• Constrained Uniform Sampling
• Policy Degeneration
• Out-of-Domain Generalization