Reinforcement Learning for LLM Reasoning Under Memory Constraints

• URL: http://arxiv.org/abs/2504.20834v1
• Date: Tue, 29 Apr 2025 14:58:43 GMT
• Title: Reinforcement Learning for LLM Reasoning Under Memory Constraints
• Authors: Alan Lee, Harry Tong,
• Abstract summary: We introduce S-GRPO, a memory-efficient variant of Group Relative Policy Optimization, and T-SPMO, a token-level prefix matching strategy for fine-grained credit assignment.<n>Despite limited resources, when used to fine-tune Qwen2-1.5B both methods significantly improve SVAMP benchmark accuracy from 46% to above 70% using LoRA training.<n>We find that our full-token GRPO baseline under LoRA fine-tuning did not improve model performance (compared to base model) on either task.
• Score: 0.02488650627593658
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We explore reinforcement learning (RL) techniques to enhance reasoning within targeted problem spaces in large language models (LLMs) under memory and compute constraints. Our focus is on critic-free methods compatible with LoRA fine-tuning on a single 40GB GPU, a common limitation in academic settings. We introduce S-GRPO, a memory-efficient variant of Group Relative Policy Optimization, and T-SPMO, a token-level prefix matching strategy for fine-grained credit assignment. Despite limited resources, when used to fine-tune Qwen2-1.5B both methods significantly improve SVAMP benchmark accuracy from 46% to above 70% using LoRA training. T-SPMO also excels in multi-digit multiplication tasks, underscoring the potential of RL fine-tuning under hardware constraints. Additionally, we find that our full-token GRPO baseline under LoRA fine-tuning did not improve model performance (compared to base model) on either task, suggesting that our memory-efficient methods may act as a form of regularization that stabilizes training when only a small subset of parameters are updated.

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