The field of large language models is moving towards more efficient and effective reasoning capabilities. Recent developments focus on reducing computational overhead, improving data efficiency, and enhancing generalizability across domains. Notable advancements include the use of self-distillation, reinforcement learning, and prototype-based reasoning. These innovations have led to significant performance gains and improved robustness in various reasoning tasks. Noteworthy papers in this area include LearnAlign, which introduces a novel gradient-alignment-based method for data-efficient reinforcement learning, and DART, which proposes a self-distillation framework for efficient chain-of-thought reasoning. TreeRL is also notable for its incorporation of on-policy tree search into reinforcement learning for large language models. Additionally, ProtoReasoning presents a framework that leverages prototypical representations to enhance generalizable reasoning in large language models. These contributions have the potential to significantly advance the field of large language models and their applications in reasoning tasks.
Efficient Reasoning in Large Language Models
Sources
History-Aware Cross-Attention Reinforcement: Self-Supervised Multi Turn and Chain-of-Thought Fine-Tuning with vLLM
LearnAlign: Reasoning Data Selection for Reinforcement Learning in Large Language Models Based on Improved Gradient Alignment