Papers with OpenAI o3
WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning (2025.emnlp-main)
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Zhepei Wei, Wenlin Yao, Yao Liu, Weizhi Zhang, Qin Lu, Liang Qiu, Changlong Yu, Puyang Xu, Chao Zhang, Bing Yin, Hyokun Yun, Lihong Li
| Challenge: | Existing work on reinforcement learning has focused on single-turn tasks such as solving math problems. |
| Approach: | They propose a framework that learns directly from online interactions by asynchronously generating diverse trajectories, guided by binary rewards depending on task success. |
| Outcome: | Experiments on the WebArena-Lite benchmark show that the framework outperforms state-of-the-art methods and strong proprietary models. |
Idola Tribus of AI: Large Language Models tend to perceive order where none exists (2025.findings-emnlp)
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| Challenge: | a tendency of large language models to generate absurd patterns is observed . authors say this is a limitation of the models' ability to perform complex tasks . |
| Approach: | We present a tendency of large language models to generate absurd patterns . authors conducted an experiment to evaluate logical consistency and self-coherence of LLMs . |
| Outcome: | a recent study shows that large language models generate absurd patterns despite their inadequacy . the model over-recognized patterns that were inconsistent with the given numbers, the study finds . |
Can Post-Training Transform LLMs into Causal Reasoners? (2026.findings-acl)
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| Challenge: | Causal inference is a core component of human cognition and requires decision-makers to distinguish between causation and association. |
| Approach: | They propose a dataset comprising seven core causal tasks for training and five diverse test sets and evaluate five different post-training approaches. |
| Outcome: | The proposed model achieves 93.5% accuracy on the CaLM benchmark, compared to 55.4% by OpenAI o3. |