How You Begin is How You Reason: Driving Exploration in RLVR via Prefix-Tuned Priors

Researchers propose IMAX, a framework that uses trainable prefix tuning to improve exploration in reinforcement learning with verifiable rewards (RLVR) for language model reasoning. The approach addresses entropy collapse by creating diverse reasoning trajectories, achieving performance gains up to 11.60% in Pass@4 accuracy across multiple model scales.

The research tackles a fundamental challenge in using reinforcement learning to improve large language model reasoning capabilities. Current RLVR systems struggle with exploration efficiency—they optimize for single correct answers but fail to discover multiple valid reasoning paths, a problem termed entropy collapse. This manifests practically as models becoming overfitted to narrow solution spaces despite having access to diverse valid approaches. The IMAX framework introduces a novel solution by training soft prefix controllers that reshape how the base model generates reasoning trajectories, rather than relying solely on reward signals to drive exploration. Each prefix acts as a distinct lens through which the same underlying model operates, enabling systematic exploration of different reasoning behaviors. The framework incorporates an Information Maximization reward signal that complements traditional verifiable rewards, encouraging the discovery of both high-quality and diverse reasoning patterns. This algorithmic contribution matters because efficient exploration directly impacts the practical utility of LLMs in complex reasoning tasks like mathematical problem-solving and code generation, where multiple solution paths exist. The consistent 10-11% improvement across different model scales suggests the approach generalizes well rather than being model-specific. The algorithm-agnostic design allows integration into existing RLVR systems without major modifications. As organizations increasingly deploy LLMs for reasoning-intensive applications, reducing training inefficiency while improving coverage becomes economically significant. The research advances the state of reinforcement learning in AI systems, potentially enabling more robust and versatile reasoning capabilities in future language models without requiring substantially larger models or computational resources.

• →IMAX framework uses trainable prefix tuning to solve entropy collapse in RLVR systems, enabling exploration of diverse reasoning trajectories
• →Information Maximization reward signal complements verifiable rewards to encourage discovery of task-relevant reasoning behaviors
• →Achieves 11.60% improvement in Pass@4 and 10.57% in Avg@4 metrics across multiple backbone model scales
• →Algorithm-agnostic design enables seamless integration into existing reinforcement learning pipelines without architectural changes
• →Addresses practical inefficiency in LLM reasoning tasks where multiple valid solution paths exist