Jin Peng

Jingyu Liu, Jingquan Peng, xiaopeng Wu et al.

Despite the widespread application of Large Language Models (LLMs) across various domains, they frequently exhibit overconfidence when encountering uncertain scenarios, yet existing solutions primarily rely on evasive responses (e.g., "I don't know") overlooks the opportunity of identifying and addressing the uncertainty to generate more satisfactory responses. To systematically investigate and improve LLMs' ability of recognizing and addressing the source of uncertainty, we introduce \textbf{ConfuseBench}, a benchmark mainly focus on three types of uncertainty: document scarcity, limited capability, and query ambiguity. Experiments with ConfuseBench reveal that current LLMs struggle to accurately identify the root cause of uncertainty and solve it. They prefer to attribute uncertainty to query ambiguity while overlooking capability limitations, especially for those weaker models. To tackle this challenge, we first generate context-aware inquiries that highlight the confusing aspect of the original query. Then we judge the source of uncertainty based on the uniqueness of the inquiry's answer. Further we use an on-policy training method, InteractDPO to generate better inquiries. Experimental results demonstrate the efficacy of our approach.

Jingyu Liu, Xiaopeng Wu, Jingquan Peng et al.

Building autonomous agents capable of solving long-horizon, real-world tasks has garnered significant research interest. But outcome based rewards may cause reward miscalibration which means it might mistakenly allocate positive reward to flawed middle steps which is regarded as the key reason making the bad actions being reinforced during training. However we reveal that outcome based reward ensures expected negative advantage for those flawed middle steps, which means the flawed actions should be punished during training. Even accounting for the ``squeezing effect", the probability mass of good actions should increase and the actor should gradually get rid of harmful actions. This shows that flawed actions should be punished during training. We further identify gradient coupling between similar samples as a key issue in agentic RL, the input prompt is extremely similar and the output action space is limited, therefore during training, gradients from well-performing samples can inadvertently strengthen suboptimal or incorrect actions due to similar input observation and output actions. We show that with gradient coupling, some flawed actions might be enhanced. To address this, we propose training the actor to classify good or bad actions to separate the embedding of good/bad actions and alleviate the gradient interference, extensive experiments shows its effectiveness.