15 citations · 37 across the 28 of their papers we have counts for
10 papers · 1 filter
Evidence-Based Policy Choice in Macroeconomics
Toru Kitagawa, Simone Martinalli
This paper studies data-driven macroeconomic policy choice in a structural VAR framework. We formulate the policymaker's problem of optimizing the future path of a policy instrumen…
FlowBalance: Verifier-Grounded Self-Improvement from On-Policy Reasoning Experience
Zixun Huang, Kishan Panaganti, Haitao Mi +1
A reasoning model can improve from its own on-policy experience, but this inner loop is fragile: terminal verifiers provide reliable yet sparse supervision, while dense same-model…
Stale but Stable: Staleness-Adaptive Trust Regions for Stabilizing Asynchronous Reinforcement Learning
Junyao Yang, Yucheng Shi, Zongxia Li +6
Asynchronous reinforcement learning improves throughput by decoupling rollout generation from optimization, but the resulting staleness is an inevitable byproduct, compounded joint…
Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable
Ruhan Wang, Yucheng Shi, Zongxia Li +7
The capability of a modern AI agent depends not only on its foundation model but also on its harness, which constructs prompts, manages state, invokes tools, and coordinates execut…
Reasoning or Memorization? Direction-Aware Diversity Exploration in LLM Reinforcement Learning
Jiangnan Xia, Yucheng Shi, Yu Yang +3
Reinforcement learning has become a key paradigm for eliciting reasoning abilities in large language models, where exploration is crucial for discovering effective solution traject…
Leveraging Machine-Learned Advice in Strategic Interactions with No-Regret Learners
Tinashe Handina, Tongxin Li, Kishan Panaganti +2
We study how an agent in a two-player repeated game can effectively utilize potentially imperfect advice when interacting with a no-regret learner. We characterize the advice lands…
Learning to Build the Environment: Self-Evolving Reasoning RL via Verifiable Environment Synthesis
Yucheng Shi, Zhenwen Liang, Kishan Panaganti +3
We pursue a vision for self-improving language models in which the model does not merely generate problems or traces to imitate, but constructs the environments that train it. In z…
The Single-Multi Evolution Loop for Self-Improving Model Collaboration Systems
Shangbin Feng, Kishan Panaganti, Yulia Tsvetkov +1
Model collaboration -- systems where multiple language models (LMs) collaborate -- combines the strengths of diverse models with cost in loading multiple LMs. We improve efficiency…
Distributionally Robust Cooperative Multi-Agent Reinforcement Learning via Robust Value Factorization
Chengrui Qu, Christopher Yeh, Kishan Panaganti +2
Cooperative multi-agent reinforcement learning (MARL) commonly adopts centralized training with decentralized execution, where value-factorization methods enforce the individual-gl…
Group Distributionally Robust Optimization-Driven Reinforcement Learning for LLM Reasoning
Kishan Panaganti, Zhenwen Liang, Wenhao Yu +2
Recent progress in Large Language Model (LLM) reasoning is increasingly driven by the refinement of post-training loss functions and alignment strategies. However, standard Reinfor…