Papers by Shaoning Sun
Distributional Clarity: The Hidden Driver of RL-Friendliness in Large Language Models (2026.acl-long)
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| Challenge: | RL-friendly models exhibit intra-class compactness and inter-class separation in probability assignments . under identical training, Qwen models achieve substantial gains, while others like Llama yield limited improvements. |
| Approach: | They propose a method to quantify distributional clarity in probability space . they show distributional clearness is a trainable property underlying RL-Friendliness . |
| Outcome: | The proposed model families achieve substantial gains under identical training, while others like Llama yield limited improvements. |
H-MEM: Hierarchical Memory for High-Efficiency Long-Term Reasoning in LLM Agents (2026.eacl-long)
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| Challenge: | Long-term memory is one of the key factors influencing the reasoning capabilities of Large Language Model Agents. |
| Approach: | They propose a hierarchical memory architecture that organizes and updates memory in a multi-level fashion based on the degree of semantic abstraction. |
| Outcome: | The proposed model outperforms baseline methods on five task settings from the LoCoMo dataset. |
Preference-Aware Memory Update for Long-Term LLM Agents (2026.findings-acl)
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| Challenge: | Existing methods for integrating long-term memory do not provide dynamic and personalized memory refinement. |
| Approach: | They propose a long-term memory update mechanism that enables dynamic and personalized memory refinement. |
| Outcome: | The proposed mechanism improves the performance of LLM-based agents in five tasks. |
A Dual-Phase Self-Evolution Framework for Large Language Models (2026.findings-acl)
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| Challenge: | Existing strategies to optimize LLMs through pretraining fail to enhance domain cognition. |
| Approach: | They propose a dual-phase self-evolution framework that integrates user preference adaptation and domain-specific competence to optimize LLMs. |
| Outcome: | The proposed framework outperforms Supervised Fine-Tuning, Preference Optimization, and Memory-Augmented baselines on general NLP benchmarks and long-term dialogue tasks. |
ProFit: Leveraging High-Value Signals in SFT via Probability-Guided Token Selection (2026.findings-acl)
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| Challenge: | Traditional fine-tuning ignores one-to-many nature of language, leading to overfitting . authors propose a method to fine- tune LLMs by leveraging tokens. |
| Approach: | They propose a method to fine-tune Large Language Models by leveraging tokens to mask low-probability tokens. |
| Outcome: | The proposed method outperforms baselines on general reasoning and mathematical benchmarks. |
Improve LLM-as-a-Judge Ability as a General Ability (2025.emnlp-main)
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| Challenge: | Recent studies focus on generative judges, but only on their judge ability. |
| Approach: | They propose a method that leverages the generative and reasoning capabilities of large language models to evaluate LLM responses across diverse scenarios, providing accurate preference signals. |
| Outcome: | The proposed model performs on RewardBench with only 2% to 40% of the data required by other training frameworks. |