RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization

2026-04-16 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, long

Summary

RL-PLUS is a novel approach designed to enhance the reasoning capabilities of Large Language Models (LLMs) in Reinforcement Learning with Verifiable Reward (RLVR) settings. Traditional RLVR methods often suffer from "capability boundary collapse," where LLMs struggle to acquire new reasoning abilities beyond their base model, leading to a narrowing of problem-solving scope. RL-PLUS addresses this by synergizing internal exploitation ("Thinking") with external data ("Learning"). It integrates Multiple Importance Sampling to manage distributional mismatch from external data and an Exploration-Based Advantage Function to guide the model toward high-value, unexplored reasoning paths. Extensive experiments show RL-PLUS achieves state-of-the-art performance on six math reasoning benchmarks and superior performance on six out-of-distribution reasoning tasks, with average relative improvements ranging from 21.1% to 69.2% across diverse model families. It also effectively resolves the capability boundary collapse problem.

Key takeaway

For AI Engineers developing LLMs for complex reasoning tasks, RL-PLUS offers a robust method to overcome the limitations of traditional RLVR. By integrating Multiple Importance Sampling and an Exploration-Based Advantage Function, your models can acquire novel reasoning abilities and avoid capability boundary collapse. Consider adopting RL-PLUS to achieve significant performance gains and enhanced generalization across diverse model families, particularly for math and coding challenges.

Key insights

RL-PLUS enhances LLM reasoning by combining internal exploitation with external data, preventing capability boundary collapse.

Principles

• Balance internal exploitation with external learning.
• Address distributional mismatch in off-policy learning.
• Incentivize exploration of low-probability, high-value paths.

Method

RL-PLUS uses Multiple Importance Sampling for unbiased reward estimation from diverse data and an Exploration-Based Advantage Function to up-weight gradients for correct, hard-to-explore reasoning paths.

In practice

• Implement Multiple Importance Sampling for off-policy data.
• Apply an Exploration-Based Advantage Function to prioritize novel solutions.

Topics

• Reinforcement Learning with Verifiable Reward
• Large Language Models
• Capability Boundary Collapse
• Multiple Importance Sampling
• Exploration-Based Advantage Function

Code references

• YihongDong/RL-PLUS

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.