RM-R1: Reward Modeling as Reasoning (2025 · 49 citations)
Reward models guide the alignment of LLMs, but each existing family has a weakness. Scalar RMs give no justification for their scores. Generative RMs explain their verdicts, yet their reasoning is often shallow. The authors treat preference judgment as a reasoning problem and introduce Reasoning Reward Models…
MEMORYLLM: Towards Self-Updatable Large Language Models (2024 · 27 citations)
Large language models are usually frozen once deployed, which makes adding fresh knowledge difficult. Current workarounds each have drawbacks. Retrieval stores keep growing, fact-level editing struggles with longer passages, and long-context approaches eventually run out of context length. This paper proposes…
Current Agents Fail to Leverage World Model as Tool for Foresight (2026 · 1 citation)
The paper asks whether today's vision-language agents can treat a generative world model as an optional external tool: a simulator they consult to preview the consequences of an action before committing to it in the real environment.
Beyond Sample-Level Feedback: Using Reference-Level Feedback to Guide Data Synthesis (2026)
Synthetic instruction-tuning data is cheap to produce, but a model trained on it usually cannot exceed the LLM that generated it.

Co-authors here

12 of 28 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

Papers of our library cite their works 71 times (of the 149,439 papers whose bibliographies we read); 30 citing papers with a page here →.

Updated 1 November 2026