Papers with test-time search
AgentRM: Enhancing Agent Generalization with Reward Modeling (2025.acl-long)
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Yu Xia, Jingru Fan, Weize Chen, Siyu Yan, Xin Cong, Zhong Zhang, Yaxi Lu, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing LLM-based agents have strong performance on held-in tasks, but their generalizability to unseen tasks remains poor. |
| Approach: | They propose a reward-based generalizable reward model to guide the policy model for effective test-time search. |
| Outcome: | The proposed agentRM outperforms existing agents on held-in tasks by 8.8 points on average. |
CoTEvol: Self-Evolving Chain-of-Thoughts for Data Synthesis in Mathematical Reasoning (2026.findings-acl)
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| Challenge: | Existing methods to improve LLMs' reasoning abilities suffer from diminishing returns or high computing overhead. |
| Approach: | They propose a genetic evolutionary framework that casts CoT generation as a population-based search over reasoning trajectories. |
| Outcome: | The proposed framework improves correct-CoT synthesis success by over 30% and enhances structural diversity with markedly improved efficiency. |