Researchers at Tencent Holdings have proposed a new “lightweight” technique to get AI models to improve by using “experience” without retraining, sparking a debate about whether that could be the key to more cost-effective continual learning. The paper titled “Training-Free Group Relative Policy Optimisation”, published last week on open-access repository arXiv, argued that large language models (LLMs) can improve through on-the-job experience, without needing to change their parameters. Current...