Large Language Models (LLMs) have demonstrated remarkable performance in code intelligence tasks such as code generation, summarization, and translation. However, their reliance on linearized token sequences makes them brittle to long-range program dependencies and superficial lexical shifts such as identifier renaming. Existing structure-aware approaches typically treat structure as serialized text prompts or auxiliary training objectives, which often inflate context length or rely on internalized structural priors, failing to provide explicit guidance during inference. To address these limitations, we propose CGBridge, a novel plug-and-play method that enhances LLMs with Code Graph information through an external, trainable Bridge module. It aligns Code Property Graph structure with code semantics and compresses them into compact soft-prefixes, decoupling structural reasoning from textual generation without updating the backbone. Experiments across multiple code LLM backbones and scales show consistent gains over both text-only adaptation and graph-augmented baselines. Furthermore, CGBridge remains robust under identifier renaming and enables over 4× faster inference than LoRA-tuned models, demonstrating both effectiveness and efficiency in structure-aware code understanding.

Large language model (LLM) agents that follow the sequential “reason-then-act” paradigm have achieved superior performance in many complex tasks. However, these methods suffer from limited exploration and incomplete environmental understanding, as they interact with only a single environment per step. In this paper, we first introduce a novel paradigm that enables an agent to interact with multiple environments simultaneously and share cross-trajectory experiences. Build upon this paradigm, we further propose Diverse Parallel Exploration Policy Optimization (DPEPO), a reinforcement learning (RL) algorithm that encourages the agent to perform diverse parallel exploration. There are two stages in DPEPO: initial supervised fine-tuning (SFT) imparts basic parallel reasoning and action generation, followed by reinforcement learning stage with a hierarchical reward scheme. We design a parallel trajectory-level success reward and two step-level rewards: Diverse Action Reward and Diverse State Transition Reward, which actively penalize behavioral redundancy and promote broad exploration. Extensive experiments on ALFWorld and ScienceWorld show that DPEPO achieves state-of-the-art (SOTA) success rates, while maintaining comparable efficiency to strong sequential baselines.