FAPO: Flawed-Aware Policy Optimization for Efficient and Reliable Reasoning. Project Page: https://fapo-rl.github.io/
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dyyyyyyyy
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updated a dataset about 10 hours ago
dyyyyyyyy/swe-rebench-filtered published a dataset about 10 hours ago
dyyyyyyyy/swe-rebench-filtered updated a dataset 24 days ago
dyyyyyyyy/r2e-gym-subset-filteredOrganizations
We introduce ScaleQuest, a scalable and novel data synthesis method. Project Page: https://scalequest.github.io/
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Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis from Scratch
Paper • 2410.18693 • Published • 42 -
dyyyyyyyy/ScaleQuest-Math
Viewer • Updated • 1M • 76 • 23 -
dyyyyyyyy/ScaleQuest-Code
Viewer • Updated • 157k • 28 • 4 -
dyyyyyyyy/ScaleQuest-Math-Qwen2.5
Viewer • Updated • 622k • 43
FAPO: Flawed-Aware Policy Optimization for Efficient and Reliable Reasoning. Project Page: https://fapo-rl.github.io/
We propose Self-Denoising Monte Carlo Annotation (SCAN), an efficient Process Reward Model (PRM) data synthesis and noise-tolerant learning framework.
We introduce ScaleQuest, a scalable and novel data synthesis method. Project Page: https://scalequest.github.io/
-
Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis from Scratch
Paper • 2410.18693 • Published • 42 -
dyyyyyyyy/ScaleQuest-Math
Viewer • Updated • 1M • 76 • 23 -
dyyyyyyyy/ScaleQuest-Code
Viewer • Updated • 157k • 28 • 4 -
dyyyyyyyy/ScaleQuest-Math-Qwen2.5
Viewer • Updated • 622k • 43
We introduce GNER, a Generative Named Entity Recognition framework, which demonstrates enhanced zero-shot capabilities across unseen entity domains.