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arxiv: 2604.25291 · v1 · submitted 2026-04-28 · 💻 cs.IR

Recognition: unknown

From Local Indices to Global Identifiers: Generative Reranking for Recommender Systems via Global Action Space

Pengyue Jia , Xiaobei Wang , Yingyi Zhang , Shuchang Liu , Yupeng Hou , Hailan Yang , Xu Gao , Xiaopeng Li
show 9 more authors
Yejing Wang Julian McAuley Xiang Li Lantao Hu Yongqi Liu Kaiqiao Zhan Han Li Kun Gai Xiangyu Zhao
Authors on Pith no claims yet

Pith reviewed 2026-05-07 15:38 UTC · model grok-4.3

classification 💻 cs.IR
keywords globalitemsrerankingactionindiceslocalphasespace
0
0 comments X

The pith

GloRank reformulates list-wise reranking as token generation over a global item identifier space, using supervised pre-training followed by reinforcement learning to maximize list-wise utility and outperforming baselines on benchmarks and industrial data.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Recommender systems often use a multi-stage process where the final reranking step decides the exact order of items shown to a user. Traditional rerankers treat this as picking positions from whatever list was fed in, so the same model output can mean different items on different days or for different users. GloRank instead turns items into fixed sequences of tokens that act like global names. The model then generates the best sequence of these names for the current context. Because the names stay the same no matter the input order, the model can learn stable preferences for each item. Training happens in two steps: first the model is shown good example rankings, then reinforcement learning directly rewards it for producing lists that users actually like. Experiments on public datasets and a large industrial system showed better performance than prior methods, especially when new items appear that the system has not seen much before.

Core claim

We propose GloRank (Global Action Space Ranker), a generative framework that shifts reranking from selecting local indices to generating global identifiers. ... Extensive experiments on two public benchmarks and a large-scale industrial dataset, coupled with online A/B tests, demonstrate that GloRank consistently outperforms state-of-the-art baselines and achieves superior robustness in cold-start scenarios.

Load-bearing premise

That representing items as sequences of discrete tokens and training via supervised pre-training plus RL will produce a stable global action space that generalizes beyond the training distribution without introducing new inconsistencies in token generation.

Figures

Figures reproduced from arXiv: 2604.25291 by Hailan Yang, Han Li, Julian McAuley, Kaiqiao Zhan, Kun Gai, Lantao Hu, Pengyue Jia, Shuchang Liu, Xiang Li, Xiangyu Zhao, Xiaobei Wang, Xiaopeng Li, Xu Gao, Yejing Wang, Yingyi Zhang, Yongqi Liu, Yupeng Hou.

Figure 1
Figure 1. Figure 1: Semantic Inconsistency in Action Spaces. view at source ↗
Figure 2
Figure 2. Figure 2: Overview of GloRank. then present our decoupled optimization pipeline in Section 3.3, comprising a supervised pre-training phase (Section 3.3.1) that ini￾tializes the policy with high-quality demonstrations, and a reward￾driven post-training phase (Section 3.3.2) that directly maximizes list-wise utility. Finally, in Section 3.4, we detail the prefix-tree constrained decoding algorithm, which effectively b… view at source ↗
Figure 3
Figure 3. Figure 3: Score distribution comparison. We observe the following: (1) GloRank exhibits a significant perfor￾mance advantage over all baselines. Its score distribution is notably higher, with both the median and the upper quartile far exceeding those of the strongest baselines. This indicates that GloRank is not only better on average but also possesses a higher ceiling for optimizing list utility. (2) Results are c… view at source ↗
Figure 4
Figure 4. Figure 4: Hyperparameter analysis. Precision NDCG MAP F1 0.2 0.4 0.6 0.8 Amazon Books Precision NDCG MAP F1 0.2 0.4 0.6 0.8 MovieLens-1M LAS Ours view at source ↗
Figure 6
Figure 6. Figure 6: Robustness comparison between GloRank and Goal view at source ↗
read the original abstract

In modern recommender systems, list-wise reranking serves as a critical phase within the multi-stage pipeline, finalizing the exposed item sequence and directly impacting user satisfaction by modeling complex intra-list item dependencies. Existing methods typically formulate this task as selecting indices from the local input list. However, this approach suffers from a semantically inconsistent action space: the same output neuron (logits) represents different items across different samples, preventing the model from establishing a stable, intrinsic understanding of the items. To address this, we propose GloRank (Global Action Space Ranker), a generative framework that shifts reranking from selecting local indices to generating global identifiers. Specifically, we represent items as sequences of discrete tokens and reformulate reranking as a token generation task. This design effectively decouples the scoring mechanism from the variable input order, ensuring that items are evaluated against a consistent global standard. We further enhance this with a two-stage optimization pipeline: a supervised pre-training phase to initialize the model with high-quality demonstrations, followed by a reinforcement learning-based post-training phase to directly maximize list-wise utility. Extensive experiments on two public benchmarks and a large-scale industrial dataset, coupled with online A/B tests, demonstrate that GloRank consistently outperforms state-of-the-art baselines and achieves superior robustness in cold-start scenarios.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The manuscript proposes GloRank, a generative reranking model for recommender systems that shifts from selecting local list indices to generating global item identifiers represented as token sequences. It employs a two-stage training process consisting of supervised pre-training on high-quality demonstrations followed by reinforcement learning to optimize list-wise utility. The paper reports consistent outperformance over state-of-the-art baselines on two public benchmarks, a large-scale industrial dataset, and through online A/B tests, with particular advantages in cold-start scenarios.

Significance. If the validity concerns are addressed, this work could be significant for the field by providing a more stable and semantically consistent action space for reranking models. The decoupling from variable input orders via global identifiers is a conceptually appealing idea, and the integration of generative modeling with RL for direct utility optimization represents a promising direction. The inclusion of industrial-scale experiments and online tests strengthens the practical implications.

major comments (1)
  1. The framework reformulates reranking as autoregressive generation over a global vocabulary, but the manuscript does not describe any mechanism to ensure that generated token sequences decode to valid and unique items from the provided candidate list (e.g., no mention of vocabulary restriction, masked decoding, or post-hoc validation in the generation or reward function). This is a load-bearing issue for the central claim, as without it the output may include invalid items, undermining the interpretation as a reranker and the claimed benefits over local-index methods.
minor comments (1)
  1. [Abstract] The abstract refers to 'two public benchmarks' without naming them; this should be specified for clarity in the introduction or experimental setup.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We sincerely thank the referee for the thoughtful and constructive review. We are encouraged by the recognition of the conceptual appeal of decoupling reranking from local indices via global identifiers, as well as the value of our two-stage training and industrial-scale validation. We address the single major comment below and will revise the manuscript accordingly to strengthen the presentation.

read point-by-point responses

  1. Referee: The framework reformulates reranking as autoregressive generation over a global vocabulary, but the manuscript does not describe any mechanism to ensure that generated token sequences decode to valid and unique items from the provided candidate list (e.g., no mention of vocabulary restriction, masked decoding, or post-hoc validation in the generation or reward function). This is a load-bearing issue for the central claim, as without it the output may include invalid items, undermining the interpretation as a reranker and the claimed benefits over local-index methods.

    Authors: We thank the referee for identifying this important implementation detail. The manuscript indeed provides insufficient description of the constrained generation process, which is a valid point. In the revised manuscript we will add a dedicated subsection (Section 3.4) that explicitly describes the following mechanisms: (1) at each autoregressive step we maintain a dynamic mask over the global vocabulary that only permits tokens consistent with completing an item identifier present in the current candidate list (implemented via a prefix trie of candidate token sequences); (2) after full sequence generation we apply a deterministic post-processing step that replaces any duplicate items with the next-highest-probability valid candidate not yet selected; and (3) the list-wise reward in the RL stage is computed exclusively on the resulting valid, unique list. These additions ensure the generated output is always a permutation of items from the provided candidate set, preserving the reranking interpretation while retaining the benefits of the global action space. We believe this clarification directly resolves the concern. revision: yes

Circularity Check

0 steps flagged

New generative reformulation with independent experimental support

full rationale

The paper introduces GloRank as a shift from local-index selection to autoregressive token generation over global item identifiers, motivated by the claimed semantic inconsistency of per-list action spaces. This is presented as a modeling choice rather than a mathematical derivation. The two-stage pipeline (supervised pre-training on demonstrations followed by RL on list-wise utility) is described as an optimization procedure whose outputs are validated externally via benchmarks, industrial data, and A/B tests. No equations or claims reduce a 'prediction' to a fitted parameter, self-citation chain, or definitional tautology. The skeptic concern about unconstrained generation producing invalid items is an implementation or correctness issue, not evidence that any load-bearing step collapses to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The approach assumes items can be losslessly tokenized into discrete sequences that preserve semantic identity across lists. No explicit free parameters or invented entities are named in the abstract; the RL reward function and token vocabulary size are implicit but not quantified here.

pith-pipeline@v0.9.0 · 5591 in / 1250 out tokens · 23707 ms · 2026-05-07T15:38:27.049447+00:00 · methodology

discussion (0)

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Reference graph

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