6 papers
ISO: An RLVR-Native Optimization Stack
Hanqing Zhu, Wenyan Cong, Zhizhou Sha +8
Reinforcement learning with verifiable rewards (RLVR) is rapidly advancing the reasoning capabilities of language models, yet the optimization layer that converts reward feedback i…
Do We Need Adam? Surprisingly Strong and Sparse Reinforcement Learning with SGD in LLMs
Sagnik Mukherjee, Lifan Yuan, Pavan Jayasinha +2
Reinforcement learning (RL), particularly RL from verifiable reward (RLVR), has become a crucial phase of training large language models (LLMs) and a key focus of current scaling e…
Graph Neural Network Assisted Genetic Algorithm for Structural Dynamic Response and Parameter Optimization
Sagnik Mukherjee, Indrajit Barua
The optimization of structural parameters, such as mass(m), stiffness(k), and damping coefficient(c), is critical for designing efficient, resilient, and stable structures. Convent…
Reinforcement Learning Finetunes Small Subnetworks in Large Language Models
Sagnik Mukherjee, Lifan Yuan, Dilek Hakkani-Tur +1
Reinforcement learning (RL) yields substantial improvements in large language models (LLMs) downstream task performance and alignment with human values. Surprisingly, such large ga…
ReasoningFlow: Semantic Structure of Complex Reasoning Traces
Jinu Lee, Sagnik Mukherjee, Dilek Hakkani-Tur +1
Large reasoning models (LRMs) generate complex reasoning traces with planning, reflection, verification, and backtracking. In this work, we introduce ReasoningFlow, a unified schem…
Infogent: An Agent-Based Framework for Web Information Aggregation
Revanth Gangi Reddy, Sagnik Mukherjee, Jeonghwan Kim +3
Despite seemingly performant web agents on the task-completion benchmarks, most existing methods evaluate the agents based on a presupposition: the web navigation task consists of…