Token-Level Credit Assignment Optimization for Generative Document Retrieval

Generative retrieval models perform document retrieval by autoregressively generating document identifiers (DocIDs). This process naturally forms a sequential decision problem, where each decoding step selects a DocID token and the complete token sequence determines the retrieved document. However, retrieval effectiveness is typically evaluated only after the full DocID is generated, creating a mismatch between token-level generation and document-level relevance supervision. As a result, existing reinforcement learning methods for generative retrieval mostly rely on sequence-level rewards, where the same document-level feedback is propagated to all decoding steps. Such coarse-grained feedback makes it difficult to identify which token decisions are responsible for successful or failed retrieval. In this work, we propose a fine-grained reinforcement learning framework for generative retrieval with token-level relevance rewards. Instead of assigning a single reward to the entire generated DocID, we estimate step-wise rewards by measuring how each token decision changes the expected retrieval quality of the corresponding generation trajectory. This enables more precise credit assignment and encourages the policy to favor token decisions that contribute more directly to document-level relevance. We further develop practical reward estimation strategies tailored to the DocID generation process and incorporate them into a policy optimization framework. Experiments on retrieval benchmarks show that our method consistently outperforms sequence-level reward baselines, demonstrating the effectiveness of fine-grained supervision for aligning autoregressive DocID generation with retrieval objectives.

DiffuGR: Generative Document Retrieval with Diffusion Language Models

Generative retrieval (GR) re-frames document retrieval as a sequence-based document identifier (DocID) generation task, memorizing documents with model parameters and enabling end-to-end retrieval without explicit indexing. Existing GR methods are based on auto-regressive generative models, i.e., the token generation is performed from left to right. However, such auto-regressive methods suffer from: (1) mismatch between DocID generation and natural language generation, e.g., an incorrect DocID token generated in early left steps would lead to totally erroneous retrieval; and (2) failure to balance the trade-off between retrieval efficiency and accuracy dynamically, which is crucial for practical applications. To address these limitations, we propose generative document retrieval with diffusion language models, dubbed DiffuGR. It models DocID generation as a discrete diffusion process: during training, DocIDs are corrupted through a stochastic masking process, and a diffusion language model is learned to recover them under a retrieval-aware objective. For inference, DiffuGR attempts to generate DocID tokens in parallel and refines them through a controllable number of denoising steps. In contrast to conventional left-to-right auto-regressive decoding, DiffuGR provides a novel mechanism to first generate more confident DocID tokens and refine the generation through diffusion-based denoising. Moreover, DiffuGR also offers explicit runtime control over the qualitylatency tradeoff. Extensive experiments on benchmark retrieval datasets show that DiffuGR is competitive with strong auto-regressive generative retrievers, while offering flexible speed and accuracy tradeoffs through variable denoising budgets. Overall, our results indicate that non-autoregressive diffusion models are a practical and effective alternative for generative document retrieval.

Temporal-Spatial Object Relations Modeling for Vision-and-Language Navigation

Vision-and-Language Navigation (VLN) is a challenging task where an agent is required to navigate to a natural language described location via vision observations. The navigation abilities of the agent can be enhanced by the relations between objects, which are usually learned using internal objects or external datasets. The relationships between internal objects are modeled employing graph convolutional network (GCN) in traditional studies. However, GCN tends to be shallow, limiting its modeling ability. To address this issue, we utilize a cross attention mechanism to learn the connections between objects over a trajectory, which takes temporal continuity into account, termed as Temporal Object Relations (TOR). The external datasets have a gap with the navigation environment, leading to inaccurate modeling of relations. To avoid this problem, we construct object connections based on observations from all viewpoints in the navigational environment, which ensures complete spatial coverage and eliminates the gap, called Spatial Object Relations (SOR). Additionally, we observe that agents may repeatedly visit the same location during navigation, significantly hindering their performance. For resolving this matter, we introduce the Turning Back Penalty (TBP) loss function, which penalizes the agent's repetitive visiting behavior, substantially reducing the navigational distance. Experimental results on the REVERIE, SOON, and R2R datasets demonstrate the effectiveness of the proposed method.

CPCL: Cross-Modal Prototypical Contrastive Learning for Weakly Supervised Text-based Person Re-Identification

Weakly supervised text-based person re-identification (TPRe-ID) seeks to retrieve images of a target person using textual descriptions, without relying on identity annotations and is more challenging and practical. The primary challenge is the intra-class differences, encompassing intra-modal feature variations and cross-modal semantic gaps. Prior works have focused on instance-level samples and ignored prototypical features of each person which are intrinsic and invariant. Toward this, we propose a Cross-Modal Prototypical Contrastive Learning (CPCL) method. In practice, the CPCL introduces the CLIP model to weakly supervised TPRe-ID for the first time, mapping visual and textual instances into a shared latent space. Subsequently, the proposed Prototypical Multi-modal Memory (PMM) module captures associations between heterogeneous modalities of image-text pairs belonging to the same person through the Hybrid Cross-modal Matching (HCM) module in a many-to-many mapping fashion. Moreover, the Outlier Pseudo Label Mining (OPLM) module further distinguishes valuable outlier samples from each modality, enhancing the creation of more reliable clusters by mining implicit relationships between image-text pairs. Experimental results demonstrate that our proposed CPCL attains state-of-the-art performance on all three public datasets, with a significant improvement of 11.58%, 8.77% and 5.25% in Rank@1 accuracy on CUHK-PEDES, ICFG-PEDES and RSTPReid datasets, respectively. The code is available at https://github.com/codeGallery24/CPCL.