Think-at-Hard: Selective Latent Iterations to Improve Reasoning Language Models

The recent introduction of the Think-at-Hard (TaH) method marks a significant advancement in improving the reasoning capabilities of Large Language Models (LLMs). This method selectively focuses on refining predictions for difficult tokens, addressing the identified issue of 'latent overthinking,' where initially correct predictions can be erroneously changed in further iterations. By employing a lightweight neural decider, TaH dynamically triggers additional iterations only for tokens deemed likely incorrect after the standard forward pass. Experiments have demonstrated that TaH significantly boosts LLM reasoning performance across five challenging benchmarks, all while preserving the same parameter count. This innovation not only enhances the practical application of LLMs in real-world scenarios but also contributes to the ongoing discourse on optimizing AI models for better reasoning and decision-making capabilities.