Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergy (2025 · 48 citations)
Open reward models used in RLHF are fragile. High RewardBench scores often fail to carry over to other evaluations, and tweaks to losses or architectures have brought little real progress. The authors argue that the bottleneck is the preference data itself, which is often narrow, synthetically labeled or poorly…
Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning (2025 · 9 citations)
Retrieval-augmented LLMs face two competing demands. They should trust retrieved evidence even when it contradicts what they memorized (adherence), and they should ignore retrieved passages that look relevant but are actually noise (robustness). Training aimed at one of these tends to hurt the other. The authors…
Skywork-SWE: Unveiling Data Scaling Laws for Software Engineering in LLMs (2025 · 3 citations)
Training LLM agents to fix real GitHub issues is held back by scarce data. Every task needs an executable environment and validated unit tests, and building these by hand is slow, so most public SWE datasets have only a few thousand instances. The authors created an automated, incremental pipeline with three stages…

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Papers of our library cite their works 58 times (of the 149,439 papers whose bibliographies we read); 25 citing papers with a page here →.

Updated 31 October 2026