A new research paper suggests that adaptive rank allocation for LoRA (Low-Rank Adaptation) methods, which is effective in supervised fine-tuning (SFT), does not translate well to reinforcement learning (RL) settings like Group Relative Policy Optimization (GRPO). When tested on the Qwen 2.5 1.5B model using the GSM8K benchmark, this adaptive approach led to a 4.5-point drop in accuracy compared to uniform allocation. The study identifies that the flatter gradient landscape in RL and a gradient amplification effect, where non-uniform allocation causes a positive feedback loop, are key reasons for this performance degradation. AI
IMPACT Suggests current fine-tuning strategies may not directly transfer to RL alignment training, requiring new approaches.
RANK_REASON Research paper published on arXiv detailing findings about model training techniques. [lever_c_demoted from research: ic=1 ai=1.0]
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