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A focused feed for RLHF, preference data, rater protocols, agent evaluation, and LLM-as-judge research. Every paper includes structured metadata for quick triage.

Total papers: 108 Search mode: keyword Shortlist (0) RSS

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Mastering Multi-Drone Volleyball through Hierarchical Co-Self-Play Reinforcement Learning

Ruize Zhang, Sirui Xiang, Zelai Xu, Feng Gao, Shilong Ji, Wenhao Tang · May 7, 2025

Citations: 0

Match reason: Matches selected tags (Demonstrations).

Score: 53% High protocol signal Freshness: Cold Status: Ready
Demonstrations Automatic Metrics Long Horizon General
  • The task is turn-based, multi-agent, and physically grounded, posing significant challenges due to its long-horizon dependencies, tight inter-agent coupling, and the underactuated dynamics of quadrotors.
Open paper
Continual Robot Skill and Task Learning via Dialogue

Weiwei Gu, Suresh Kondepudi, Anmol Gupta, Lixiao Huang, Nakul Gopalan · Sep 5, 2024

Citations: 0

Match reason: Matches selected tags (Demonstrations).

Score: 50% Moderate protocol signal Freshness: Cold Status: Ready
Demonstrations Simulation Env General
  • In this work we present a framework for robots to continually learn tasks and visuo-motor skills and query for novel skills via dialog interactions with human users.
  • Moreover, with our IRB approved human-subjects study we demonstrate that our dialog based continual learning framework allows users to teach robots cooking skills successfully (100%) while spending a higher ratio of time on finishing an…
Open paper
VolleyBots: A Testbed for Multi-Drone Volleyball Game Combining Motion Control and Strategic Play

Zelai Xu, Ruize Zhang, Chao Yu, Huining Yuan, Xiangmin Yi, Shilong Ji · Feb 4, 2025

Citations: 0

Match reason: Matches selected tags (Demonstrations).

Score: 53% High protocol signal Freshness: Cold Status: Fallback
Demonstrations Automatic MetricsSimulation Env Multi Agent General
  • We provide a comprehensive suite of tasks ranging from single-drone drills to multi-drone cooperative and competitive tasks, accompanied by baseline evaluations of representative reinforcement learning (RL), multi-agent reinforcement…
  • Simulation results show that on-policy RL methods outperform off-policy methods in single-agent tasks, but both approaches struggle in complex tasks that combine motion control and strategic play.
Open paper
CowPilot: A Framework for Autonomous and Human-Agent Collaborative Web Navigation

Faria Huq, Zora Zhiruo Wang, Frank F. Xu, Tianyue Ou, Shuyan Zhou, Jeffrey P. Bigham · Jan 28, 2025

Citations: 0

Match reason: Matches selected tags (Demonstrations).

Score: 53% High protocol signal Freshness: Cold Status: Fallback
Pairwise PreferenceDemonstrations Automatic Metrics Web Browsing General
  • We propose CowPilot, a framework supporting autonomous as well as human-agent collaborative web navigation, and evaluation across task success and task efficiency.
  • We conducted case studies on five common websites and found that the human-agent collaborative mode achieves the highest success rate of 95% while requiring humans to perform only 15.2% of the total steps.
Open paper
Inverse Reinforcement Learning with Dynamic Reward Scaling for LLM Alignment

Ruoxi Cheng, Haoxuan Ma, Weixin Wang, Ranjie Duan, Jiexi Liu, Xiaoshuang Jia · Mar 23, 2025

Citations: 0

Match reason: Matches selected tags (Demonstrations).

Score: 46% Sparse protocol signal Freshness: Cold Status: Fallback
Pairwise PreferenceDemonstrations General
  • Existing techniques are either reward-based (training a reward model on preference pairs and optimizing with reinforcement learning) or reward-free (directly fine-tuning on ranked outputs).
  • Recent research shows that well-tuned reward-based pipelines remain the most robust, and single-response demonstrations can outperform pairwise preference data.
Open paper