Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty (2026 · 8 citations)
Generative reward models (GRMs) judge LLM outputs using chain-of-thought (CoT) reasoning. They have two weaknesses. They spend reasoning effort on every input, including easy ones. They also pick among sampled reasoning chains by voting, which cannot separate sound chains from ones that only look convincing. The…
Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models (2026 · 6 citations)
Supervised fine-tuning (SFT) is usually treated as a reliable way to specialize LLMs. The authors show, however, that converged models still get part of their own training set wrong. They call this the Incomplete Learning Phenomenon (ILP), distinguish it from catastrophic forgetting and machine unlearning, and…
Beyond Token Length: Step Pruner for Efficient and Accurate Reasoning in Large Language Models (2026 · 2 citations)
Large Reasoning Models often "overthink," producing very long chains of thought even for trivial questions.
Universally Empowering Zeroth-Order Optimization via Adaptive Layer-wise Sampling (2026)
Zeroth-Order (ZO) fine-tuning of LLMs needs only forward passes and therefore uses about as much memory as inference.

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Updated 1 November 2026