3 citations · 8 across the 10 of their papers we have counts for
10 papers
GEAR: Granularity-Adaptive Advantage Reweighting for LLM Agents via Self-Distillation
Sijia Li, Yuchen Huang, Zifan Liu +7
Reinforcement learning has become a widely used post-training approach for LLM agents, where training commonly relies on outcome-level rewards that provide only coarse supervision.…
GAS: Enhancing Reward-Cost Balance of Generative Model-assisted Offline Safe RL
Zifan Liu, Xinran Li, Shibo Chen +1
Offline Safe Reinforcement Learning (OSRL) aims to learn a policy to achieve high performance in sequential decision-making while satisfying constraints, using only pre-collected d…
Experience-Evolving Multi-Turn Tool-Use Agent with Hybrid Episodic-Procedural Memory
Sijia Li, Yuchen Huang, Zifan Liu +6
As intents unfold and environments change, multi-turn agents face continuously shifting decision contexts. Although reusing past experience is intuitively appealing, existing appro…
TSDS: Data Selection for Task-Specific Model Finetuning
Zifan Liu, Amin Karbasi, Theodoros Rekatsinas
Finetuning foundation models for specific tasks is an emerging paradigm in modern machine learning. The efficacy of task-specific finetuning largely depends on the selection of app…
Reinforcement Learning with Intrinsically Motivated Feedback Graph for Lost-sales Inventory Control
Zifan Liu, Xinran Li, Shibo Chen +3
Reinforcement learning (RL) has proven to be well-performed and general-purpose in the inventory control (IC). However, further improvement of RL algorithms in the IC domain is imp…
Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement Learning
Xinran Li, Zifan Liu, Shibo Chen +1
In multi-agent reinforcement learning (MARL), effective exploration is critical, especially in sparse reward environments. Although introducing global intrinsic rewards can foster…
Rapidash: Efficient Constraint Discovery via Rapid Verification
Zifan Liu, Shaleen Deep, Anna Fariha +3
Denial Constraint (DC) is a well-established formalism that captures a wide range of integrity constraints commonly encountered, including candidate keys, functional dependencies,…
AutoSlicer: Scalable Automated Data Slicing for ML Model Analysis
Zifan Liu, Evan Rosen, Paul Suganthan G. C
Automated slicing aims to identify subsets of evaluation data where a trained model performs anomalously. This is an important problem for machine learning pipelines in production…
Picket: Guarding Against Corrupted Data in Tabular Data during Learning and Inference
Zifan Liu, Zhechun Zhou, Theodoros Rekatsinas
Data corruption is an impediment to modern machine learning deployments. Corrupted data can severely bias the learned model and can also lead to invalid inferences. We present, Pic…
On Robust Mean Estimation under Coordinate-level Corruption
Zifan Liu, Jongho Park, Theodoros Rekatsinas +1
We study the problem of robust mean estimation and introduce a novel Hamming distance-based measure of distribution shift for coordinate-level corruptions. We show that this measur…