15 citations · 65 across the 65 of their papers we have counts for
16 papers · 1 filter
EvoX: Meta-Evolution for Automated Discovery
Shu Liu, Shubham Agarwal, Monishwaran Maheswaran +14
Recent work such as AlphaEvolve has shown that combining LLM-driven optimization with evolutionary search can effectively improve programs, prompts, and algorithms across domains.…
Reward Under Attack: Analyzing the Robustness and Hackability of Process Reward Models
Rishabh Tiwari, Aditya Tomar, Udbhav Bamba +5
Process Reward Models (PRMs) are rapidly becoming the backbone of LLM reasoning pipelines, yet we demonstrate that state-of-the-art PRMs are systematically exploitable under advers…
Jet-RL: Enabling On-Policy FP8 Reinforcement Learning with Unified Training and Rollout Precision Flow
Haocheng Xi, Charlie Ruan, Peiyuan Liao +7
Reinforcement learning (RL) is essential for enhancing the complex reasoning capabilities of large language models (LLMs). However, existing RL training pipelines are computational…
CDLM: Consistency Diffusion Language Models For Faster Sampling
Minseo Kim, Chenfeng Xu, Coleman Hooper +5
Diffusion Language Models (DLMs) offer a promising parallel generation paradigm but suffer from slow inference due to numerous refinement steps and the inability to use standard KV…
Beyond Next-Token Prediction: A Performance Characterization of Diffusion versus Autoregressive Language Models
Minseo Kim, Coleman Hooper, Aditya Tomar +5
Large Language Models (LLMs) have achieved state-of-the-art performance on a broad range of Natural Language Processing (NLP) tasks, including document processing and code generati…
SciML Agents: Write the Solver, Not the Solution
Saarth Gaonkar, Xiang Zheng, Haocheng Xi +5
Recent work in scientific machine learning aims to tackle scientific tasks directly by predicting target values with neural networks (e.g., physics-informed neural networks, neural…