Researchers have proposed the Never Give Up method for training LLM

Researchers presented the Never Give Up method aimed at eliminating the "Matthew effect" when training LLM with reinforcement learning (RL). The problem is that current algorithms effectively optimize simple tasks, but lose the learning signal on complex cases, where the model makes mistakes more often.

The essence of the approach is to change the sampling strategy when generating responses. Instead of standard training on a limited dataset, the system forcibly increases the number of generation attempts for complex tasks. This allows the model to receive a gradient signal even in cases where the probability of success is initially close to zero, preventing premature convergence to suboptimal strategies.

During the experiments, it was found that with standard RL training, models stop progressing on tasks with high cognitive load after the first iterations. The Never Give Up method allows maintaining the stability of training, ensuring uniform improvement of metrics on both simple and complex requests. The authors note that this approach requires additional computational power at the data collection stage, but significantly improves the final accuracy of the model.

This technique is aimed at solving the fundamental problem of reward hacking and performance degradation in narrow areas. A detailed discussion of architectural limitations and testing results is available in the Hacker News community. The implementation of such mechanisms allows developers to more effectively scale model training to complex logical chains and multi-step tasks.