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20242026
most citedDoes Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning

1 citations · 3 across the 30 of their papers we have counts for

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cs.LG2026

Stateless Language Agents: Scaling Long-Horizon Automated Research

Qizheng Zhang, Changxiu Ji, Isaac Sun +15

Automated research systems increasingly run LLM agents over long horizons, but more inference does not by itself produce more progress: agents replay growing histories, duplicate o…

cs.LG2026

AIBL: Augmented Instance-Based Learning with Structured Memory and Neural Embeddings

Radha Poovendran, Andrea Stocco, Linda Bushnell

Sequential learning systems often make decisions from accumulated experience while receiving high-dimensional inputs whose distribution may change over time. Instance-Based Learnin…

cs.LG2026

Polyhedral Instability Governs Regret in Online Learning

Yuetai Li, Fengqing Jiang, Yichen Feng +6

Many online decision problems over combinatorial actions are addressed via convex relaxations, leading to online convex optimization with piecewise linear objectives and induced po…

cs.LG2026

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks

Fengqing Jiang, Yuetai Li, Yichen Feng +6

Hypergraphs provide a natural framework to model higher-order interactions in scientific, social, and biological systems. Hypergraph neural networks (HGNNs) aim to learn from such…

cs.LG2026

When the Strongest Teacher Is Not the Best Teacher: Student-Centric Answer Selection

Zhengyu Hu, Zheyuan Xiao, Linxin Song +10

LLM training increasingly relies on teacher-generated supervision, from synthetic responses to reasoning traces and tool-use demonstrations. Current practice often chooses the high…

cs.LG2025

TOUCAN: Synthesizing 1.5M Tool-Agentic Data from Real-World MCP Environments

Zhangchen Xu, Adriana Meza Soria, Shawn Tan +4

Large Language Model (LLM) agents are rapidly emerging as powerful systems for automating tasks across domains. Yet progress in the open-source community is constrained by the lack…

cs.LG2025

TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning

Zhangchen Xu, Yuetai Li, Fengqing Jiang +4

Reinforcement Learning (RL) has become a powerful tool for enhancing the reasoning abilities of large language models (LLMs) by optimizing their policies with reward signals. Yet,…

cs.LG2025

SoSBench: Benchmarking Safety Alignment on Six Scientific Domains

Fengqing Jiang, Fengbo Ma, Zhangchen Xu +8

Large language models (LLMs) exhibit advancing capabilities in complex tasks, such as reasoning and graduate-level question answering, yet their resilience against misuse, particul…

cs.LG2025

KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding

Zhangchen Xu, Yang Liu, Yueqin Yin +2

We introduce KodCode, a synthetic dataset that addresses the persistent challenge of acquiring high-quality, verifiable training data across diverse difficulties and domains for tr…

cs.LG2024

A Method for Fast Autonomy Transfer in Reinforcement Learning

Dinuka Sahabandu, Bhaskar Ramasubramanian, Michail Alexiou +3

This paper introduces a novel reinforcement learning (RL) strategy designed to facilitate rapid autonomy transfer by utilizing pre-trained critic value functions from multiple envi…