Three recent papers on arXiv address efficiency challenges in fine-tuning large language models (LLMs).
TokenSeek (arXiv:2601.19739v1) proposes an “instance-aware token ditching” approach to reduce memory consumption during fine-tuning. According to the paper, fine-tuning is considered “a de facto approach for adapting large language models to downstream tasks,” but high training memory consumption inherited from LLMs makes the process inefficient.
Reinforcement Learning Fine-Tuning Study (arXiv:2509.21044v2) examines how different fine-tuning methods affect LLM internal circuitry. The paper states that LLMs acquire knowledge through pretraining and can be enhanced via supervised fine-tuning (SFT) or reinforcement learning (RL)-based post-training, with findings showing RL fine-tuning “enhances activation intensity and diversity” in internal model circuitry.
RPO (Reinforcement Fine-Tuning with Partial Reasoning Optimization) (arXiv:2601.19404v1) addresses computational overhead in reinforcement fine-tuning. According to the abstract, traditional reinforcement fine-tuning algorithms require “generation of a complete reasoning trajectory beginning from the input query,” which creates “significant computational overhead during the rollout phase.” RPO aims to optimize this process by focusing on partial reasoning trajectories.
All three papers target reducing resource requirements for adapting LLMs to specific tasks while maintaining or improving model performance.