3 citations · 3 across the 15 of their papers we have counts for
11 papers · 1 filter
Failure Is the Mother of Success: Recovering Supervision with Grounding Hindsight
Sheldon Yu, Tong Yu, Nikki Lijing Kuang +6
When every rollout for a problem fails, GRPO receives neither a successful exemplar nor a nonzero group-relative advantage, so it learns nothing from exactly the problems it most n…
SafeGEO: Understanding Generative Engine Optimization Risks in Recommendation Agents
Qianfeng Wen, Yifan Simon Liu, Xin Liu +4
Generative Engine Optimization (GEO) lets content owners rewrite web content to increase their visibility in generative systems. In recommendation agents, this creates a risk that…
RankJudge: A Multi-Turn LLM-as-a-Judge Synthetic Benchmark Generator
Zhenwei Tang, Zhaoyan Liu, Rasa Hosseinzadeh +3
As interactive LLM-based applications are created and refined, model developers need to evaluate the quality of generated text along many possible axes. For simpler systems, human…
Chessformer: A Unified Architecture for Chess Modeling
Daniel Monroe, George Eilender, Philip Chalmers +2
Chess has long served as a canonical testbed for artificial intelligence, but modeling approaches for its central tasks have diverged. Maximizing playing strength, predicting human…
MINER: Mining Multimodal Internal Representation for Efficient Retrieval
Weien Li, Rui Song, Zeyu Li +8
Visual document retrieval has become essential for accessing information in visually rich documents. Existing approaches fall into two camps. Late-interaction retrievers achieve st…
LLM Safety From Within: Detecting Harmful Content with Internal Representations
Difan Jiao, Yilun Liu, Ye Yuan +4
Guard models are widely used to detect harmful content in user prompts and LLM responses. However, state-of-the-art guard models rely solely on terminal-layer representations and o…
Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learning
Rohan Surana, Gagan Mundada, Xunyi Jiang +19
Reinforcement learning (RL) has become a central post-training tool for improving the reasoning abilities of large language models (LLMs). In these systems, the rollout, the trajec…
ThinkTwice: Jointly Optimizing Large Language Models for Reasoning and Self-Refinement
Difan Jiao, Qianfeng Wen, Blair Yang +2
We introduce ThinkTwice, a simple two-phase framework that jointly optimizes LLMs to solve reasoning problems and refine the answers, based on Group Relative Policy Optimization (G…
Grounded Chess Reasoning in Language Models via Master Distillation
Zhenwei Tang, Qianfeng Wen, Seth Grief-Albert +4
Language models often lack grounded reasoning capabilities in specialized domains where training data is scarce but bespoke systems excel. We introduce a general framework for dist…
MineDraft: A Framework for Batch Parallel Speculative Decoding
Zhenwei Tang, Arun Verma, Zijian Zhou +4
Speculative decoding (SD) accelerates large language model inference by using a smaller draft model to propose draft tokens that are subsequently verified by a larger target model.…
Level Up: Defining and Exploiting Transitional Problems for Curriculum Learning
Amogh Inamdar, Zhenwei Tang, Ashton Anderson +1
Curriculum learning--ordering training examples in a sequence to aid machine learning--takes inspiration from human learning, but has not gained widespread acceptance. Static strat…