33 papers · * denotes equal contribution
NeurIPS 2026
TREST combines evolutionary search with supervised fine-tuning to prioritize robust reasoning trajectories, favoring insight-driven mathematical strategies over brittle solutions.
NeurIPS 2026
MIPU addresses training-inference mismatch in LLM reinforcement learning through sampler-referenced updates and inference-gap-aware acceptance, improving reasoning performance and training stability.
NeurIPS 2026
RLDP estimates problem difficulty from hidden activations in a single forward pass, while RLDP-AdaSwitch uses that signal to allocate reasoning effort efficiently.
DAI 2026 Oral
An agent-ensemble approach to scaling reinforcement learning by combining multiple agents with complementary behaviors.
ICML 2026 Spotlight
ScaleMoE brings a mixture-of-experts architecture to actor-critic reinforcement learning, scaling network capacity to improve sample efficiency and performance on continuous control tasks.
SPOT@ICLR 2026
DistRLVR is a distributional RL framework for LLM post-training with verifiable rewards that models token-level return distributions and uses tail-aware advantages to improve sample efficiency and reasoning performance.
ICLR 2026
An LLM-driven multi-agent system for end-to-end single-cell and spatial transcriptomics analysis through natural language, combining hierarchical planning, expert tools, and self-reflective optimization.
ICLR 2026
ReMix brings off-policy reinforcement finetuning to LLM post-training by reusing rollout data from past policies, dramatically reducing training cost while staying competitive on math reasoning benchmarks.
LLA@ICLR 2026
AgentMemoryBench is a unified benchmark for continual agent memory that measures improvement, retention, forgetting, transfer, and conflict resolution over time, together with a multi-memory baseline called MEMs.
WWW 2026
AFE-Master introduces domain-specific language parsing with guided local search to strengthen LLM-driven autonomous feature engineering.
KDD 2026
PACE leverages code embeddings to enhance LLM-driven AutoML agents, improving feature engineering and model selection through structured code representations.
NeurIPS 2025
A reward-modeling approach for preference-based reinforcement learning built on proximal policy exploration.
ICCV 2025
RoboAnnotatorX presents a comprehensive and universal framework for annotating long-horizon robot demonstrations to enable accurate understanding.
ACL 2025
A dual-process framework that combines retrieval and reasoning to improve multi-hop question answering.
ACL 2025
Investigates how competitive mechanisms enhance the reasoning capabilities of LLMs, leading to improved performance on complex tasks.
AAAI 2025
Analyzes mediator roles and decisive voices within multi-agent debate frameworks, revealing how influence shifts throughout deliberation.
WWW 2025
SheetAgent is a generalist LLM agent for spreadsheet reasoning and manipulation across realistic multi-step tasks.
IJCAI 2024
An experience resampling method that uses gradient-direction uncertainty for more stable policy improvement.
IJCAI 2024
An offline-to-online reinforcement learning method that improves transition efficiency with Q-ensembles.
AAMAS 2024
Organizing samples in a trajectory manner can substantially improve the learning efficiency for offline RL algorithms.
AAAI 2024
OVD-Explorer achieves noise-aware optimistic exploration by separating useful uncertainty from stochastic noise for continuous control.
NeurIPS 2024
A holistic language-and-vision planning framework that unifies perception, reasoning, imagination, and action for manipulation.
ICML 2024
A unified keyframe identification and skill annotation method for long-horizon robot demonstrations.
NeurIPS 2024 Workshop
A preference-based reinforcement learning study on improving reward models with proximal policy exploration.
ICLR 2024
A unified platform and benchmark suite for reinforcement learning with diverse human feedback.
TNNLS 2023
A survey of exploration methods in deep reinforcement learning, spanning single-agent and multi-agent settings.
CAAI AIR 2023
An offline reinforcement learning method that stays conservative on out-of-distribution states for sequential decision-making.
ICLR 2023
An unsupervised reinforcement learning method that improves efficiency with a multi-choice dynamics model.
ASE 2021
A deep reinforcement learning approach for generating failure-inducing inputs in cyber-physical systems testing.