Bidirectional Entropy Modulation: Rethinking Exploration in Reinforcement Learning for LLM Reasoning

Research on reinforcement learning with verifiable rewards (RLVR) has revealed a fundamental limitation: policies rapidly converge to narrow solution sets. A new paper proposes decomposing policy entropy into "informative" and "spurious" components, enabling more effective exploration without blind entropy maximization.

The Problem

RLVR has significantly advanced LLM reasoning (think o1-style reasoning). However:

• Restricted exploration — The policy quickly converges to a narrow set of solutions
• Entropy regularization is unreliable — Standard entropy bonuses have high hyperparameter sensitivity and yield marginal gains
• Blind maximization hurts — Simply increasing entropy degrades reasoning quality

The Key Insight

The paper decomposes policy entropy into two types:

1. Informative entropy — Preserves diverse solution paths (good)
2. Spurious entropy — Erodes reasoning patterns (bad)

Effective exploration requires maximizing informative entropy while minimizing spurious entropy — not blindly maximizing total entropy.

How It Works

• Group-relative advantage estimation — Parametric formulation to distinguish useful vs harmful exploration
• Entropy dynamics analysis — Track how entropy changes during training
• Bidirectional modulation — Simultaneously encourage diverse solutions while preserving reasoning structure

Results

The approach shows improved performance on reasoning benchmarks compared to standard entropy regularization, particularly on problems requiring diverse solution strategies.