A study explored the effectiveness of Supervised Fine-Tuning (SFT) and Proximal Policy Optimization (Grpo) on the Qwen3.5 4B model for arithmetic tasks. The results indicated that SFT led to improved test accuracy, while three separate reward-based tuning runs using Grpo performed below the SFT validation baseline. AI
IMPACT This research provides insights into fine-tuning techniques for smaller language models on specific tasks.
RANK_REASON The cluster contains a research paper detailing experiments with fine-tuning an existing model. [lever_c_demoted from research: ic=1 ai=1.0]
Read on Medium — fine-tuning tag →
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