Shichun Liu

According to our database1, Shichun Liu authored at least 28 papers between 2023 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
Entropy Polarity in Reinforcement Fine-Tuning: Direction, Asymmetry, and Control.
CoRR, May, 2026

CL-bench Life: Can Language Models Learn from Real-Life Context?
CoRR, April, 2026

Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses.
CoRR, April, 2026

EVPO: Explained Variance Policy Optimization for Adaptive Critic Utilization in LLM Post-Training.
CoRR, April, 2026

MM-Doc-R1: Training Agents for Long Document Visual Question Answering through Multi-turn Reinforcement Learning.
CoRR, April, 2026

Can RL Improve Generalization of LLM Agents? An Empirical Study.
CoRR, March, 2026

DFPO: Scaling Value Modeling via Distributional Flow towards Robust and Generalizable LLM Post-Training.
CoRR, February, 2026

ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development.
CoRR, January, 2026

LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models.
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2026

DARM: Distribution-Aware Reward Modeling by Alleviating Biases from Low Preference-Context Dependency Data.
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2026

OctoBench: Benchmarking Scaffold-Aware Instruction Following in Repository-Grounded Agentic Coding.
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2026

MetaAct-RL: Training Language Models for Reasoning Through Meta-Action-Based Reinforcement Learning.
Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025
Memory in the Age of AI Agents.
CoRR, December, 2025

DVPO: Distributional Value Modeling-based Policy Optimization for LLM Post-Training.
CoRR, December, 2025

LLMEval-3: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models.
CoRR, August, 2025

Pre-Trained Policy Discriminators are General Reward Models.
CoRR, July, 2025

EvaLearn: Quantifying the Learning Capability and Efficiency of LLMs via Sequential Problem Solving.
CoRR, June, 2025

EvaLearn: Quantifying the Learning Capability and Efficiency of LLMs via Sequential Problem Solving.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Pre-Trained Policy Discriminators are General Reward Models.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2025, 2025

Multi-Programming Language Sandbox for LLMs.
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), 2025

Lost in the Context: Insufficient and Distracted Attention to Contexts in Preference Modeling.
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

2024
Multi-Programming Language Sandbox for LLMs.
CoRR, 2024

Self-Demos: Eliciting Out-of-Demonstration Generalizability in Large Language Models.
Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2024, 2024

Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer Capabilities.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

LLMEval: A Preliminary Study on How to Evaluate Large Language Models.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models.
CoRR, 2023


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