When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Tasks (2023 · 42 citations)
Large language models are mostly used through in-context learning (ICL), but its limits are poorly mapped.
Agentic Reward Modeling: Integrating Human Preferences with Verifiable Correctness Signals for Reliable Reward Systems (2025 · 18 citations)
Reward models for LLMs are usually trained only on human preferences. They can favour superficial traits such as longer or more polished answers and miss factual mistakes or ignored constraints. The authors propose agentic reward modeling, in which the final reward is a weighted combination of a base preference score…
VerIF: Verification Engineering for Reinforcement Learning in Instruction Following (2025 · 18 citations)
Reinforcement learning with verifiable rewards (RLVR) works well in math and code, but how to verify instruction following has received little study.
MAVEN-FACT: A Large-scale Event Factuality Detection Dataset (2024 · 6 citations)
Event Factuality Detection (EFD) asks whether a mentioned event actually happened, might happen, or did not happen.
WildReward: Learning Reward Models from In-the-Wild Human Interactions (2026)
Reward models for LLM training usually depend on large sets of human-annotated preference pairs, which are expensive to collect.

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Papers of our library cite their works 74 times (of the 149,439 papers whose bibliographies we read); 23 citing papers with a page here →.

Updated 1 November 2026