6 citations · 10 across the 17 of their papers we have counts for
13 papers · 1 filter
AlphaDiana: A Measurement System for Agents in Verifiable Reasoning Tasks
Zhanke Zhou, Zongze Li, Weikai Huang +16
Agents pair reasoning models with harnesses that organize model calls, execute tools, load skill instructions, and retain history in memory. Understanding agent performance require…
AutoEnvScaling: Automating the Data Flywheel with Terminal Agents
Simon Yu, Baolin Peng, Bo Liu +8
Terminal agents solve tasks in software engineering and scientific research by executing commands in a terminal. Training them via reinforcement learning (RL) requires diverse and…
Evolving in Thought Space: Training a Small Model at Test Time Unlocks Better Discoveries
Chonghe Jiang, Ao Qu, Siyuan Liu +9
Open-ended scientific discovery often requires repeatedly proposing and evaluating candidate solutions. LLM-based systems can support this process by generating and refining execut…
RefCon: Iterative Refinement and Contrastive Memory Extraction for Context-Evolving Agent
Ubaidillah Ariq Prathama, Bo Liu, Yeo Boon Hong +3
Long-horizon agent interactions generate useful but noisy experience, and retraining models to absorb it is expensive. Context-evolving agents therefore need memory extraction meth…
Bayes-Sufficient Compression Is Not Enough: How Does Communication Help Multi-Agent Systems?
Yi Xie, Zhanke Zhou, Yi Fan +3
Multi-agent LLM systems pair a sender with broad context and an executor with a limited local view. We study when a short message improves the executor's next decision, when raw co…
SPADE: Self-Play in Adaptive Synthetic Executable Environments
Bo Liu, Simon Yu, Yiding Jiang +15
Continuous self-improvement requires an ever-expanding pool of self-generated, diverse, adaptive goals. For language agents, existing training environment pools (hand-curated, stat…
PERFOPT-Bench: Evaluating Coding Agents on Software Performance Optimization
Yingyun Cui, Yi Xie, Piaohong Wang +3
Coding-agent benchmarks have largely measured whether agents can produce functionally correct patches, but production software also demands measurable speedups on real execution ta…
Learning Stateful Predictive Knowledge From Experience
Yan Song, Xidong Feng, Bo Liu +7
As large language model (LLM) agents increasingly learn from experience, they primarily rely on trajectory-level reflection to extract insights. Viewed through the lens of predicti…
TeamTR: Trust-Region Fine-Tuning for Multi-Agent LLM Coordination
Yi Xie, Siao Liu, Falong Fan +3
Multi-agent LLM systems have shown promise for complex reasoning, yet recent evaluations reveal they often underperform single-model baselines. We identify a structural failure mod…
SAT: Sequential Agent Tuning for Coordinator Free Plug and Play Multi-LLM Training with Monotonic Improvement Guarantees
Yi Xie, Yangyang Xu, Yi Fan +1
Large language models (LLMs) with a large number of parameters achieve strong performance but are often prohibitively expensive to deploy. Recent work explores using teams of small…
From to : Investigating Reinforcement Learning in Pre-train Space
Yuqiao Tan, Minzheng Wang, Bo Liu +5
While reinforcement learning with verifiable rewards (RLVR) significantly enhances LLM reasoning by optimizing the conditional distribution P(y|x), its potential is fundamentally b…
: Better Prompt Optimization with Fewer Prompts
Zhaolin Gao, Yu, Wang +4
Prompt optimization improves language models without updating their weights by searching for a better system prompt, but its effectiveness varies widely across tasks. We study what…
Examining Reasoning LLMs-as-Judges in Non-Verifiable LLM Post-Training
Yixin Liu, Yue Yu, DiJia Su +7
Reasoning LLMs-as-Judges, which can benefit from inference-time scaling, provide a promising path for extending the success of reasoning models to non-verifiable domains where the…