1 citations · 2 across the 9 of their papers we have counts for
9 papers
AutoPDE: Reliable Agentic PDE Solving via Explicitly Represented Solver Strategies
Huanshuo Dong, Keyao Zhang, Hong Wang +6
Numerical solvers for partial differential equations (PDEs) are core computational tools in science and engineering. Building reliable PDE solvers requires not only executable code…
BAGEN: Are LLM Agents Budget-Aware?
Yuxiang Lin, Zihan Wang, Mengyang Liu +9
While agents are increasingly spending more resources, today agent cost is mostly measured only after execution. A Budget-Aware Agent (BAGEN) should treat budget as an active contr…
RAGEN-2: Reasoning Collapse in Agentic RL
Zihan Wang, Chi Gui, Xing Jin +13
RL training of multi-turn LLM agents is inherently unstable, and reasoning quality directly determines task performance. Entropy is widely used to track reasoning stability. Howeve…
Seed-Prover 1.5: Mastering Undergraduate-Level Theorem Proving via Learning from Experience
Jiangjie Chen, Wenxiang Chen, Jiacheng Du +19
Large language models have recently made significant progress to generate rigorous mathematical proofs. In contrast, utilizing LLMs for theorem proving in formal languages (such as…
Seed-Prover: Deep and Broad Reasoning for Automated Theorem Proving
Luoxin Chen, Jinming Gu, Liankai Huang +33
LLMs have demonstrated strong mathematical reasoning abilities by leveraging reinforcement learning with long chain-of-thought, yet they continue to struggle with theorem proving d…
Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning
ByteDance Seed, :, Jiaze Chen +267
We introduce Seed1.5-Thinking, capable of reasoning through thinking before responding, resulting in improved performance on a wide range of benchmarks. Seed1.5-Thinking achieves 8…
Heimdall: test-time scaling on the generative verification
Wenlei Shi, Xing Jin
An AI system can create and maintain knowledge only to the extent that it can verify that knowledge itself. Recent work on long Chain-of-Thought reasoning has demonstrated great po…
Flaming-hot Initiation with Regular Execution Sampling for Large Language Models
Weizhe Chen, Zhicheng Zhang, Guanlin Liu +6
Since the release of ChatGPT, large language models (LLMs) have demonstrated remarkable capabilities across various domains. A key challenge in developing these general capabilitie…
Process Supervision-Guided Policy Optimization for Code Generation
Ning Dai, Zheng Wu, Renjie Zheng +7
Reinforcement learning (RL) with unit test feedback has enhanced large language models' (LLMs) code generation, but relies on sparse rewards provided only after complete code evalu…