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Quantile Advantage Estimation: Stabilizing RLVR for LLM Reasoning
🤖AI Summary
Researchers propose Quantile Advantage Estimation (QAE) to stabilize Reinforcement Learning with Verifiable Rewards (RLVR) for large language model reasoning. The method replaces mean baselines with group-wise K-quantile baselines to prevent entropy collapse and explosion, showing sustained improvements on mathematical reasoning tasks.
Key Takeaways
- →QAE replaces mean baseline with K-quantile baseline to stabilize RLVR training for LLM reasoning tasks.
- →The method prevents both entropy collapse and entropy explosion through two-sided entropy safety bounds.
- →QAE creates a two-regime system that reinforces rare successes on hard queries while targeting failures on easy queries.
- →Empirical results show sustained pass@1 gains on Qwen3-8B/14B-Base models across AIME 2024/2025 and AMC 2023 benchmarks.
- →The research identifies baseline design as the primary mechanism for scaling RLVR rather than token-level heuristics.
#reinforcement-learning#llm#reasoning#training-stability#quantile-estimation#entropy#mathematical-reasoning#rlvr
Read Original →via arXiv – CS AI
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