Large Language Models Can Self-Improve in Long-context Reasoning
Authors: name: Siheng Li, name: Cheng Yang, name: Zesen Cheng, name: Lemao Liu, name: Mo Yu, name: Yujiu Yang, name: Wai Lam

Large language models (LLMs) have achieved substantial progress in processing
long contexts but still struggle with long-context reasoning. Existing
approaches typically involve fine-tuning LLMs with synthetic data, which
depends on annotations from human experts or advanced models like GPT-4, thus
restricting further advancements. To address this issue, we investigate the
potential for LLMs to self-improve in long-context reasoning and propose \ours,
an approach specifically designed for this purpose. This approach is
straightforward: we sample multiple outputs for each question, score them with
Minimum Bayes Risk, and then apply supervised fine-tuning or preference
optimization based on these outputs. Extensive experiments on several leading
LLMs demonstrate the effectiveness of \ours, with an absolute improvement of
4.2 points for Llama-3.1-8B-Instruct. Furthermore, \ours achieves superior
performance compared to prior approaches that depend on data produced by human
experts or advanced models. We anticipate that this work will open new avenues
for self-improvement techniques in long-context scenarios, which are essential
for the continual advancement of LLMs.