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Yining Lu

@yininglu.bsky.social
36 followers 187 following 16 posts

Second year CS PhD student @notredame.bsky.social | Intern: Amazon | Prev: @jhuclsp.bsky.social yining610.github.io

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Yining Lu @yininglu.bsky.social · 12/11/2025
🚀 one-line command for easy deployment: github.com/yining610/Re...
github.com
GitHub - yining610/Reliable-dRAG: Official repo for the paper "A Decentralized Retrieval Augmented Generation System with Source Reliabilities Secured on Blockchain"
Official repo for the paper "A Decentralized Retrieval Augmented Generation System with Source Reliabilities Secured on Blockchain" - yining610/Reliable-dRAG
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Yining Lu @yininglu.bsky.social · 12/11/2025
📣 My first system paper 📣 We built a decentralized RAG system that solves data reliability challenges in real-world settings. The sources provided by each data owner will be securely managed and scored on the blockchain. 🔗 Paper link: arxiv.org/abs/2511.07577
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Yining Lu @yininglu.bsky.social · 16/09/2025
Work done during an internship at @amazon. Huge thanks to my mentor, @zlwang_cs, and advisor, @Meng_CS, for their support in making this work possible, and to collaborators @ShiyangLi5, Xin Liu, Changlong Yu, @YinQingyu, Zhan Shi, and @zhangzxUIUC for their valuable feedback!
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Yining Lu @yininglu.bsky.social · 16/09/2025
8/8 [Convergence rate] The gradient-based method consistently has a higher convergence rate, reducing the required steps by 6.1 on average across RL algorithms.
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Yining Lu @yininglu.bsky.social · 16/09/2025
7/8 [Generalizability] We further extend experiments to different math datasets and model families. Our two methods yield superior Pareto fronts compared to the baseline, with the gradient-based weighting showing the best overall performance.
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Yining Lu @yininglu.bsky.social · 16/09/2025
6/8 [Gradient-based weight optimization] Our method generates superior Pareto fronts that dominate all baseline approaches under both GRPO and REINFORCE training.
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Yining Lu @yininglu.bsky.social · 16/09/2025
5/8 [Hypervolume-guided weight adaptation] Across all three online RL algorithms, there is consistently at least one weight configuration our method outperforms the baselines on all objectives.
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Yining Lu @yininglu.bsky.social · 16/09/2025
Dynamic reward weights show objectives learn differently. For example, accuracy is a more challenging objective that requires continual learning, while conciseness quickly converges to 0.2. 4/8
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Yining Lu @yininglu.bsky.social · 16/09/2025
3/8 [Preliminary finding] Different objectives vary in learning difficulty. Each objective reaches saturation at different training stages.
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Yining Lu @yininglu.bsky.social · 16/09/2025
Question: How to redirect learning effort towards objectives with the greatest potential for improvement. Answer: - If the user preference for objectives is given, use our hypervolume-based method - If the user preference is unknown, use our gradient-based method. 2/8
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Yining Lu @yininglu.bsky.social · 16/09/2025
✴️ Pleased to introduce our new paper yining610.github.io/dynamic-rew... - Rebalance multiobjectives during training through dynamic reward weighting - Build Pareto-dominant front over static baselines across online RL algorithms, datasets, and model families - Faster convergence rate 1/8
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Yining Lu @yininglu.bsky.social · 25/07/2025
This is our teaser video 😀 youtu.be/TgloG4Oefeg
youtube.com
ACL2025: Optimizing Decomposition for Optimal Claim Verification
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Yining Lu @yininglu.bsky.social · 25/07/2025
Can't make it to #ACL2025 this year, but for people interested in RL for factuality and textual decomposition, please check out our paper! TL;DR: We found a mismatch between the decomposition policy and LLM verifier, and propose a dynamic training paradigm to bridge the gap.
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Yining Lu @yininglu.bsky.social · 16/05/2025
Pleased to share that two papers were accepted to #ACL2025 main! Huge congratulations to all collaborators for the hard work and time we put in together! 1. Dynamic Decomposition: arxiv.org/abs/2503.15354 2. RATIONALYST: arxiv.org/abs/2410.01044 Both works study the mulit-model collobration!
arxiv.org
Optimizing Decomposition for Optimal Claim Verification
Current research on the \textit{Decompose-Then-Verify} paradigm for evaluating the factuality of long-form text typically treats decomposition and verification in isolation, overlooking their interact...
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Yining Lu @yininglu.bsky.social · 02/05/2025
Quick reminder that our paper, Benchmarking Language Model Creativity: A Case Study on Code Generation, will be presented today! 📅 11AM-12:30PM, Fri, May 2 📍 Hall 3 📝 arxiv.org/abs/2407.09007 🎥 www.youtube.com/watch?v=v1c...
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Reposted by Yining Lu
Daniel Khashabi @danielkhashabi.bsky.social · 28/04/2025
Highlighting our #NAACL2025 papers 🧵🧵🧵
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Yining Lu @yininglu.bsky.social · 28/04/2025
I will be at #NAACL2025 to present our LLM creativity benchmark. Drop by if interested (Poster Session 8, Fri, May 2)! I'd love to chat about RL and its interpretability, data influence for post-training, CogSci for LLM. Feel free to reach out and let's have some coffee together ☕ !
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Reposted by Yining Lu
Daniel Khashabi @danielkhashabi.bsky.social · 28/04/2025
A video teaser of @Yining__Lu 's paper: www.youtube.com/watch?v=v1c...
youtube.com
Benchmarking Language Model Creativity: A Case Study on Code Generation --- NAACL 2025 (Yining Lu)
Yining Lu: https://yining610.github.io/ Based on the following paper: https://arxiv.org/abs/2407.09007 As LLMs become increasingly prevalent, it is interesti...
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Reposted by Yining Lu
dchiang.bsky.social @dchiang.bsky.social · 08/03/2025
Midwest Speech and Language Days will be held Apr 15-16 at @NotreDame! Abstract submissions are due Mar 20, and registration deadline is Mar 27. Financial assistance for students (lodging, poster printing) is available. nlp.nd.edu/msld25
nlp.nd.edu
Midwest Speech and Language Days 2025
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Reposted by Yining Lu
Maria Antoniak @mariaa.bsky.social · 04/11/2024
A starter pack for #NLP #NLProc researchers! 🎉 go.bsky.app/SngwGeS
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