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LoRA rank allocation fails in RL fine-tuning, study finds

By PulseAugur Editorial · [1 sources] ·

A new study on the Qwen 2.5 1.5B model reveals that adaptive rank allocation techniques, effective in supervised fine-tuning, do not translate to reinforcement learning with Group Relative Policy Optimization (GRPO). Researchers found that proportional rank allocation under GRPO decreased accuracy by 4.5 percentage points compared to uniform allocation. This is attributed to a flatter gradient landscape in GRPO, where all layers retain meaningful gradient signals, and a gradient amplification effect that further widens importance disparities, silencing lower-rank layers. AI

IMPACT Findings suggest current fine-tuning methods for supervised learning may not directly apply to alignment training, potentially requiring new approaches for RL-based fine-tuning.

RANK_REASON Academic paper detailing empirical study of model fine-tuning techniques. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LoRA rank allocation fails in RL fine-tuning, study finds

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Academic paper detailing empirical study of model fine-tuning techniques. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Yash Ganpat Sawant ·

    Gradient-Based LoRA Rank Allocation Under GRPO: An Empirical Study

    Adaptive rank allocation for LoRA, allocating more parameters to important layers and fewer to unimportant ones, consistently improves efficiency under supervised fine-tuning (SFT). We investigate whether this success transfers to reinforcement learning, specifically Group Relati…

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