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cs.LG2026
PhantomEnvironments: Training LLM Agents in Fictional Worlds
Anmol Kabra, Swathi Saravana Selvam, Albert Gong +5
Training LLM agents with reinforcement learning (RL) is bottlenecked by environments, which must provide verifiable rewards, support long-horizon interaction, and scale cheaply. Ex…
cs.LG2026
Learning from Synthetic Data Improves Multi-hop Reasoning
Anmol Kabra, Yilun Yin, Albert Gong +6
Reinforcement Learning (RL) has been shown to significantly boost reasoning capabilities of large language models (LLMs) in math, coding, and multi-hop reasoning tasks. However, RL…
cs.LG2025
PhantomWiki: On-Demand Datasets for Reasoning and Retrieval Evaluation
Albert Gong, Kamilė Stankevičiūtė, Chao Wan +6
High-quality benchmarks are essential for evaluating reasoning and retrieval capabilities of large language models (LLMs). However, curating datasets for this purpose is not a perm…