12 papers
Toward Skill-Native LLMs: Skill Entropy for Benchmarking and Training Long-Horizon Reasoning
Yinghui He, Ling Yang, Jiarui Liu +6
Long-horizon reasoning in recent LLMs demands that the model switch between distinct skills inside a reasoning chain, such as first doing a math derivation, then using the result t…
Transferability for General Reasoning: An Automated Curriculum for Multi-Domain RLVR
Yongjin Yang, Jiarui Liu, Yinghui He +3
Reinforcement learning with verifiable rewards (RLVR) has been extended from single-domain training to multi-domain reasoning suites spanning mathematics, programming, and science.…
MixSD: Mixed Contextual Self-Distillation for Knowledge Injection
Jiarui Liu, Lechen Zhang, Yongjin Yang +5
Supervised fine-tuning (SFT) is widely used to inject new knowledge into language models, but it often degrades pretrained capabilities such as reasoning and general-domain perform…
Re-Centering Humans in LLM Personalization
Lechen Zhang, Jiarui Liu, Tal August
Despite growing interest, most evaluations of large language models' (LLMs') personalization abilities have relied on synthetic data. It remains unclear how well current personaliz…
Logit Arithmetic Elicits Long Reasoning Capabilities Without Training
Yunxiang Zhang, Muhammad Khalifa, Lechen Zhang +5
Large reasoning models exhibit long chain-of-thought reasoning with complex strategies such as backtracking and self-verification. Yet, these capabilities typically require resourc…
SPRIG: Improving Large Language Model Performance by System Prompt Optimization
Lechen Zhang, Tolga Ergen, Lajanugen Logeswaran +2
Large Language Models (LLMs) have shown impressive capabilities in many scenarios, but their performance depends, in part, on the choice of prompt. Past research has focused on opt…