1 citations · 2 across the 12 of their papers we have counts for
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Environmental Feedback Modeling Matters: Rethinking Feedback Treatment in Agentic Hindsight Self-Distillation
Hangxi Guo, Fengyuan Liu, Yue Wang +4
Reinforcement learning is commonly used to train language agents in interactive environments, but cannot be directly applied when rewards are unavailable. Recent methods use enviro…
From a Prompt to Repertoires: Evolving Functional REpertoires Enable LLM Continual Learning
Fengyuan Liu, Yue Wang, Hangxi Guo +3
Continual learning remains challenging for large language models, which must enable models to acquire new skills and knowledge without degrading existing capabilities. Existing app…
MM-FinEval: A Multi-Task Multimodal Benchmark for Real-World Financial Forecasting
Dong Shu, Yanguang Liu, Huopu Zhang +4
Financial forecasting from earnings conference calls requires models to reason over complex corporate disclosures, market expectations, and subtle communication signals. However, e…
Look Before You Select: Rethinking Vocabulary Sparsification in On-Policy Distillation
Yongliang Miao, Shuang Liu, Yanguang Liu +2
On-policy distillation (OPD) uses teacher correction on student-generated responses. Full-vocabulary correction can provide important corrections even for tokens that the student a…
BiasReducer: Adaptive Bias Mitigation for Reward Models
Shuang Liu, Yongliang Miao, Yanguang Liu +2
Reward models score responses from large language models (LLMs) and guide LLM training toward human preferences. However, reward models can favor superficial attributes such as len…
DynaCF: Mitigating Shortcut Learning in Reward Models via Dynamic Counterfactual Sensitivity
Fengyuan Liu, Yongliang Miao, Zirui He +3
Reward models trained from pairwise preferences often exploit superficial shortcut cues rather than learning true response quality. We propose DynaCF, a dynamic reweighting framewo…
SAEExplainer: Interpreting SAE Features with Activation-Guided Preference Optimization
Jingyi He, Haiyan Zhao, Ruxue Shi +4
Although Sparse Autoencoders (SAEs) have mitigated the opacity of large language models (LLMs) by decomposing dense representations into sparse features, explaining these features…
RASFT: Rollout-Adaptive Supervised Fine-Tuning for Reasoning
Yongliang Miao, Fengyuan Liu, Wei Shi +4
Supervised fine-tuning (SFT) is a prevailing method for adapting large language models to reasoning tasks by imitating offline expert demonstrations, often treating a single expert…
HARVE: Hacking-Aware Reward-Head Vector Editing for Robust Reward Models
Shuang Liu, Yuxuan Bo, Qiuyang Zhao +4
Reward models are central to large language model (LLM) alignment, but they remain vulnerable to reward hacking. To evaluate reward-model robustness, we introduce RewardHackBench c…
The Model Shape Behind Scaling Laws: A Gradient Superposition Perspective
Wenjie Sun, Jinning Yang, Shuai Zhang +1
Neural scaling laws guide resource allocation, but how should a fixed parameter budget be divided between depth and width? Motivated by inverse-width representation-error scaling u…
Universal Activation Verbalizer: A Unified Framework for Cross-Model Activation Explanation
Haiyan Zhao, Zirui He, Guanchu Wang +3
Activation verbalization explains hidden representations in natural language, but existing methods are mostly limited to self-explanation, where each model explains only its own ac…
FinAnchor: Aligned Multi-Model Representations for Financial Prediction
Zirui He, Huopu Zhang, Yanguang Liu +2
Financial prediction from long documents involves significant challenges, as actionable signals are often sparse and obscured by noise, and the optimal LLM for generating embedding…