17 citations · 26 across the 14 of their papers we have counts for
8 papers · 1 filter
Arcadia: Toward a Full-Lifecycle Framework for Embodied Lifelong Learning
Minghe Gao, Juncheng Li, Yuze Lin +15
We contend that embodied learning is fundamentally a lifecycle problem rather than a single-stage optimization. Systems that optimize only one link (data collection, simulation, le…
SPICE: Self-Play In Corpus Environments Improves Reasoning
Bo Liu, Chuanyang Jin, Seungone Kim +7
Self-improving systems require environmental interaction for continuous adaptation. We introduce SPICE (Self-Play In Corpus Environments), a reinforcement learning framework where…
The Automated LLM Speedrunning Benchmark: Reproducing NanoGPT Improvements
Bingchen Zhao, Despoina Magka, Minqi Jiang +20
Rapid advancements in large language models (LLMs) have the potential to assist in scientific progress. A critical capability toward this endeavor is the ability to reproduce exist…
Bridging Offline and Online Reinforcement Learning for LLMs
Jack Lanchantin, Angelica Chen, Janice Lan +9
We investigate the effectiveness of reinforcement learning methods for finetuning large language models when transitioning from offline to semi-online to fully online regimes for b…
Self-Challenging Language Model Agents
Yifei Zhou, Sergey Levine, Jason Weston +2
Large language models are quickly becoming the foundation for intelligent agents that are capable of using tools. However, training such agents is challenging because it requires h…
J1: Incentivizing Thinking in LLM-as-a-Judge via Reinforcement Learning
Chenxi Whitehouse, Tianlu Wang, Ping Yu +4
The progress of AI is bottlenecked by the quality of evaluation, making powerful LLM-as-a-Judge models a core solution. The efficacy of these judges depends on their chain-of-thoug…
LLM Pretraining with Continuous Concepts
Jihoon Tack, Jack Lanchantin, Jane Yu +7
Next token prediction has been the standard training objective used in large language model pretraining. Representations are learned as a result of optimizing for token-level perpl…
Learning to Plan & Reason for Evaluation with Thinking-LLM-as-a-Judge
Swarnadeep Saha, Xian Li, Marjan Ghazvininejad +2
LLM-as-a-Judge models generate chain-of-thought (CoT) sequences intended to capture the step-bystep reasoning process that underlies the final evaluation of a response. However, du…