FAPO: Flawed-Aware Policy Optimization for Efficient and Reliable Reasoning. Project Page: https://fapo-rl.github.io/
Ding
dyyyyyyyy
AI & ML interests
None yet
Organizations
ScaleQuest
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 ⢠40 ⢠23 -
dyyyyyyyy/ScaleQuest-Code
Viewer ⢠Updated ⢠157k ⢠14 ⢠4 -
dyyyyyyyy/ScaleQuest-Math-Qwen2.5
Viewer ⢠Updated ⢠622k ⢠9
COLDQA
SCAN
We propose Self-Denoising Monte Carlo Annotation (SCAN), an efficient Process Reward Model (PRM) data synthesis and noise-tolerant learning framework.
GNER
We introduce GNER, a Generative Named Entity Recognition framework, which demonstrates enhanced zero-shot capabilities across unseen entity domains.
-
Rethinking Negative Instances for Generative Named Entity Recognition
Paper ⢠2402.16602 ⢠Published ⢠3 -
dyyyyyyyy/GNER-LLaMA-7B
Text Generation ⢠7B ⢠Updated ⢠6 ⢠5 -
dyyyyyyyy/GNER-T5-base
Text Generation ⢠0.2B ⢠Updated ⢠3 ⢠2 -
dyyyyyyyy/GNER-T5-large
Text Generation ⢠0.8B ⢠Updated ⢠3 ⢠2
FAPO
FAPO: Flawed-Aware Policy Optimization for Efficient and Reliable Reasoning. Project Page: https://fapo-rl.github.io/
SCAN
We propose Self-Denoising Monte Carlo Annotation (SCAN), an efficient Process Reward Model (PRM) data synthesis and noise-tolerant learning framework.
ScaleQuest
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 ⢠40 ⢠23 -
dyyyyyyyy/ScaleQuest-Code
Viewer ⢠Updated ⢠157k ⢠14 ⢠4 -
dyyyyyyyy/ScaleQuest-Math-Qwen2.5
Viewer ⢠Updated ⢠622k ⢠9
GNER
We introduce GNER, a Generative Named Entity Recognition framework, which demonstrates enhanced zero-shot capabilities across unseen entity domains.
-
Rethinking Negative Instances for Generative Named Entity Recognition
Paper ⢠2402.16602 ⢠Published ⢠3 -
dyyyyyyyy/GNER-LLaMA-7B
Text Generation ⢠7B ⢠Updated ⢠6 ⢠5 -
dyyyyyyyy/GNER-T5-base
Text Generation ⢠0.2B ⢠Updated ⢠3 ⢠2 -
dyyyyyyyy/GNER-T5-large
Text Generation ⢠0.8B ⢠Updated ⢠3 ⢠2
COLDQA