Prejudge-Before-Think: Enhancing Large Language Models at Test-Time by Process Prejudge Reasoning (2025)

Abstract: In this paper, we introduce a new process prejudge strategy in LLM reasoning to demonstrate that bootstrapping with process prejudge allows the LLM to adaptively anticipate the errors encountered when advancing the subsequent reasoning steps, similar to people sometimes pausing to think about what mistakes may occur and how to avoid them, rather than relying solely on trial and error. Specifically, we define a prejudge node in the rationale, which represents a reasoning step, with at least one step that follows the prejudge node that has no paths toward the correct answer. To synthesize the prejudge reasoning process, we present an automated reasoning framework with a dynamic tree-searching strategy. This framework requires only one LLM to perform answer judging, response critiquing, prejudge generation, and thought completion. Furthermore, we develop a two-phase training mechanism with supervised fine-tuning (SFT) and reinforcement learning (RL) to further enhance the reasoning capabilities of LLMs. Experimental results from competition-level complex reasoning demonstrate that our method can teach the model to prejudge before thinking and significantly enhance the reasoning ability of LLMs .

## introduction large language models (llms) have made great inroads in solving natural language processing (nlp) tasks @xcite @xcite , but still struggle to produce accurate answers to complex reasoning problems. prior research tackles this challenge by designing well-crafted prompts to elicit the llm to follow a step-by-step thinking paradigm, such as in-context learning @xcite , chain-of-thought @xcite @xcite , and agentic learning @xcite . however, these let @xmath0, we can ... so, @xmath1 is 5 or -1. ## methodology in this section, we introduce a new reasoning method named prejudge before think (pbt), which aims to elicit the llm pauses to deeply consider what errors will occur and how to bypass them instead of excessive trial and error. hence, we pose three questions to illustrate how our approach works: q: three vertices of a cube are @xmath2 , @xmath3 , and @xmath4 . what is the surface area of the cube? let's think step by step, and put the final answer within \boxed{}. ## discussions we thus provide discussions about this works. ## conclusion in this paper, we present a novel paradigm of "prejudge before thinking" inspired by system 2's slow thinking mode. we propose synthesizing training data using a dynamic tree-searching method with a small llm and introduce a two-phase post-training strategy to enhance the model's reasoning ability with sft and rl techniques. we conduct extensive experiments on multiple competition-level complex reasoning benchmarks, and the results demonstrate that the rationale embedded with the prejudged hints can guide the llm to avoid making mistakes.