Token-Efficient RL for LLM Reasoning

Alan Lee; Harry Tong

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arXiv · 2504.20834

Token-Efficient RL for LLM Reasoning

Alan Lee·Harry Tong

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Abstract

We propose reinforcement learning (RL) strategies tailored for reasoning in large language models (LLMs) under strict memory and compute limits, with a particular focus on compatibility with LoRA fine-tuning. Building on early policy gradient methods with baseline subtraction, we design critic-free methods that operate on a small, informative subset of output tokens to reduce memory usage and stabilize training. We introduce S-GRPO, a stochastic variant of Group Relative Policy Optimization, and T-SPMO, a token-level prefix matching approach for fine-grained credit assignment. Applied to Qwen2-1.5B, our methods raise accuracy on the SVAMP benchmark from 46% to over 70% and show strong performance on multi-digit multiplication. Surprisingly, full-token GRPO under LoRA fails to improve over the base model, suggesting that selective token-level optimization may act as an implicit regularizer in low-parameter training regimes.

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Alan Lee, Harry Tong. 2025-04-29. Token-Efficient RL for LLM Reasoning. https://arxiv.org/abs/2504.20834

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