War of Thoughts: Competition Stimulates Stronger Reasoning in Large Language Models (2025)

Abstract: Recent advances in Large Language Models (LLMs) have reshaped the landscape of reasoning tasks, particularly through test-time scaling (TTS) to enhance LLM reasoning. Prior research has used structures such as trees or graphs to guide LLMs in searching for optimal solutions. These methods are time-consuming and require a strong reward model (RM) to support effective solution space exploration. Tournament-style approaches eliminate the reliance on RMs through comparative evaluation but suffer from transitivity dilemmas, leading to unstable ordering. To address these issues, we propose War of Thoughts (**WoT**), a novel post-hoc method that enhances reasoning without finetuning. WoT comprises two distinct stages: (1) *Exploration*, in which diverse and meaningful candidate solutions are generated through contrastive demonstrations and multi-granularity reasoning specifications; and (2) *Competition*, where these candidate solutions are subjected to multiple rounds of matchups within a competitive arena. Throughout this iterative process, the solutions are optimized and improved, with the optimal solution being determined based on Elo ratings. Extensive experiments across various LLMs demonstrate the superiority of WoT, surpassing baselines by **10–30%**. WoT can effectively stimulate stronger reasoning abilities, achieving impressive TTS performance in both generation budget and model size. It shows higher scalability efficiency compared to the baseline within the same budget. Notably, WoT exhibits excellent scalability with model size, even outperforming a 72B model despite using a 7B model.

## introduction large language models (llms) @xcite @xcite @xcite and their * both authors contributed equally to this research † corresponding to: yan zheng (yanzheng@tju.edu.cn) input best-of-n (bon) input tree-of-thought (tot) input mcts-based backpropagate rollout search based competition based input tournament war of thoughts (wot) ranking by elo @xmath0... input base score: 1500 1533 1498 1468 1498 1557 1441 1498 1533 1468 > > 1557 1533 1498 > 1441 : intermediate step : accepted solution : rejected solution : full cot solution (2) tournament-style approaches assume transitivity in comparative evaluations, which may not always hold. existing post-hoc strategies for improving the reasoning capabilities of llms can be mainly divided into two categories: (1) search-based methods and (2) competition-based methods, as illustrated in figure 1 . search-based methods, such as best-of-n (bon) @xcite , involve sampling n responses from an llm and choosing the best response based on the evaluation of rm. tot, got, and mcts-based approaches @xcite @xcite decompose problem-solving into multiple steps. intermediate steps are assessed via prm or specified scoring strategies, and the optimal solution is chosen as the final answer. however, these approaches often fail to fully explore the solution space, getting trapped in low-quality reasoning steps due to limited reflection mechanisms. for instance, increasing sample size in bon may lead to diminishing returns and even negative yield. besides, a superior rm matters; without accurate rewards, tot and mcts-based methods struggle to determine the promising solution. however, a powerful rm requires deliberate annotation @xcite , which consumes expensive resources. moreover, when employing heuristics for reward assignment, even the strongest llms can suffer from systematic biases @xcite and overconfidence @xcite . an intuitive solution is to replace scoring with comparison to lessen the burden on llms when handling complicated scoring criteria. by directly comparing the pros and cons of different solutions, llms can make more intuitive judgments. this focuses on assessing relative differences, which avoids the complexity of assigning absolute scores and reduces systematic biases. @xcite proposed a single-elimination tournament framework to rank different llm outputs. such design inherently assumes transitivity-if a beats b and b beats c, then a is expected to beat c. nevertheless, this assumption does not always hold, especially when one model significantly outperforms a second, which is closely matched with a third. therefore, this approach is unstable for determining the best solution from a single llm. to address the aforementioned issues, we introduce war of thoughts (wot), a novel tts approach that improves reasoning without finetuning. wot consists of two main stages: (1) generate diverse and meaningful grouped kickoff solutions ( § 3.1) and (2) select the optimal solution via iterative competition ( § 3.2). in stage 1, we identify common types of errors in problemsolving and construct contrastive cot demonstrations to prevent the llm from repeating similar mistakes during reasoning. meanwhile, we define multi-granularity reasoning specifications to maximize the reasoning boundaries of llms. through these two levels of guidance, we hope to enable the llm to generate multiple meaningful candidate responses, with each response focusing on a diverse path of reasoning. in stage 2, we group multiple candidates and have them compete in the arena, using multi-round matchups to alleviate positional bias in comparative evaluations. through matchup reviews, the solutions are iteratively refined and improved. moreover, we incorporate elo rating, which transforms unstable partial orders into more reliable, quantifiable rankings. extensive experiments ( § 4) across different llms and varying reasoning-intensive benchmarks demonstrate the superiority of wot, achieving a significant improvement of 10-30% over baselines such as bon, tot, rap, and rstar. we also provide additional analysis to comfirm the impressive scalability of wot ( § 5), revealing that wot scales more effectively under the same generation budget. furthermore, the 7b model with wot outperforms the 72b model despite a 10× more parameter discrepancy, highlighting wot's high scalability with respect to model size. ## related work reasoning with llms. recent years have seen a rapid improvement in llm performance on reasoning tasks @xcite @xcite @xcite . several key factors have contributed to these advancements: (1) running continuous pretraining on large corpus of reasoning tasks @xcite ; (2) utilizing a wellpretrained llm to synthesis data and progressively enhancing performance via sft @xcite @xcite @xcite ; (3) leveraging advanced prompting techniques, such as tot @xcite , rap @xcite , and rstar @xcite , which improve performance through self-exploration at inference time. however, these methods require large amounts of high-quality data or rely on a powerful rm, limiting their generalizability. our approach introduces an iterative competition framework, which bypasses rms through comparative evaluation and more effectively enhances llm reasoning at test time. answer selection. majority voting @xcite ) is a widely used method for selecting the correct reasoning trajectory. several works @xcite train rms for verification, but these require additional annotations and suffer from limited generalizability. other research @xcite designs criteria and prompts llms to score, which can lead to overconfidence @xcite . comparative evaluation has been employed to address this issue @xcite . however, it neglects positional bias @xcite and the lack of transitivity @xcite . our method introduce an elo-based rating system and use multi-round matchups to mitigate these issues. ## wot as illustrated in figure 2 , wot consists of two main stages: (1) generate diverse and meaningful grouped kickoff solutions and (2) select the optimal solution via iterative competition. in stage 1, we generate kickoff solutions by constructing various task-specific contrastive demonstrations and defining different reasoning granularity specifications. this enhances the diversity and significance of the solutions from the level of problem-solving perspective and granularity. in stage 2, we introduce the elo rating system to build a reliable ranking system. grouped candidates are engaged in pairwise matchups, update their elo scores through comparative evaluation, and improve the solution quality through reflection in an iterative process. the solution with the highest elo score is finally selected as the optimal solution. ## conclusion we propose wot, a novel method that enhances llm reasoning during inference without finetuning. we group llms as candidates and have them compete in pairwise matchups. futhermore, we introduce elo ratings to transform unstable partial orders into reliable and quantifiable rankings. extensive experiments across various llm backbones and reasoning tasks demonstrate the superiority of wot. additional analysis highlights its scalability, rationality, and shows that competition effectively stimulates stronger reasoning in llms.