GoalRank: Scalable Generator-Only Ranking

• GoalRank is a generator-only ranking framework that replaces traditional two-stage pipelines with an end-to-end approach for efficient, scalable recommendations.
• It leverages entropy-regularized expected reward maximization and group-relative normalization to minimize KL divergence with the oracle ranking policy.
• Empirical evaluations show 15–25% improvements in key metrics and lower latency, demonstrating its practical advantages in large-scale applications.

GoalRank refers to a class of methods and frameworks for large-scale ranking—particularly in the context of recommender systems—which are motivated by theoretical, algorithmic, and practical limitations of traditional generator–evaluator (two-stage) ranking architectures. The GoalRank framework is predicated on the premise that a generator-only, one-stage model, if afforded sufficient capacity, can surpass any (finite) generator–evaluator systems both in fidelity to the optimal ranking policy and empirical performance. GoalRank introduces group-relative optimization objectives, leveraging a reward model and group-based normalization to achieve robust, scalable, and production-ready ranking solutions (Zhang et al., 26 Sep 2025).

1. Generator-Only Versus Generator–Evaluator Paradigms

Mainstream ranking models often employ a two-stage pipeline: a generator produces multiple candidate lists, and an evaluator selects the most promising one, sometimes aggregating results from multiple generators. Performance gains using this approach saturate rapidly as the number of candidates increases, reflecting the inherent combinatorial complexity of the underlying list-space. GoalRank departs from this tradition by formally analyzing the expressivity of generator-only models as compared to finite generator–evaluator pipelines.

The theoretical result demonstrates that, for any (finite) MG-E (Multi-Generator–Evaluator) model with capacity parameters (α, β) and number of generators k, there exists a generator-only model (ℱ_M(α, β, n)) with sufficiently large parameter count such that its worst-case approximation error (measured as the KL divergence to the oracle optimal ranking policy π*) is strictly lower, and asymptotically approaches zero as model capacity increases:

$𝓔(ℱ_M(α, β, n)) < 𝓔(𝒞ₘ^k(α, β)),\quad\text{with}\quad\lim_{n\to\infty} 𝓔(ℱ_M(α, β, n)) = 0.$

This theoretical perspective justifies the scaling law for generator-only approaches and motivates abandoning complex hierarchical generator–evaluator cascades for sufficiently expressive models.

2. Entropy-Regularized One-Stage Optimization Objective

GoalRank adopts an end-to-end, generator-only training objective inspired by entropy-regularized expected reward maximization. Let $r^*(l)$ denote the (inaccessible) ground-truth reward assigned to a candidate ranking list $l$, and let $\tau > 0$ be the entropy regularization weight. The optimal stochastic policy $\pi^*$ maximizing expected reward and entropy is

$$\pi^*(l) = \frac{\exp(r^*(l)/\tau)}{Z},\quad Z = \sum_{l'} \exp(r^*(l')/\tau),$$

yielding a Boltzmann distribution over lists. This maximization is equivalent to minimizing the KL divergence between the parameterized policy $\pi_\theta$ and the Boltzmann oracle $\pi^*$:

$𝓛(\pi_\theta) = D_{KL}(\pi^* \| \pi_\theta).$

Since $r^*(l)$ is not available during training, a reward model $\hat{r}(l)$ (supervised via real user feedback) is substituted, and the normalization is performed within groups of candidate lists rather than globally. The training loss is instantiated as cross-entropy against a group-relative reference policy $\pi^{ref}(l|\mathcal{B})$ (see below).

3. Group-Relative Reference Policy

The evidence upper bound used for training is predicated on a practical group-relative reference policy. For each group of candidate ranking lists $\mathcal{B}$ (where each list $l \in \mathcal{B}$), the reference policy is defined as

$$\pi^{ref}(l | \mathcal{B}) = \frac{\exp\left( (\hat{r}(l) - \bar{r}_\mathcal{B}) / \sigma_\mathcal{B} \right)}{\sum_{l' \in \mathcal{B}} \exp\left( (\hat{r}(l') - \bar{r}_\mathcal{B}) / \sigma_\mathcal{B}\right) },$$

where $\bar{r}_\mathcal{B}$ and $\sigma_\mathcal{B}$ are the mean and standard deviation of the reward model $\hat{r}$ over $\mathcal{B}$.

This group-relative normalization ensures that relative utility differences—rather than potentially biased absolute reward values—drive the policy updates. It is particularly robust to miscalibration or systematic errors in the reward model, as only the relative ordering within each group matters when backpropagating gradients.

4. Empirical Evaluation and Scaling Properties

GoalRank was evaluated on both public and large-scale industrial datasets. The following findings were reported:

• Offline Experiments: On MovieLens-1M, Amazon-Books, and industry datasets, GoalRank outperforms state-of-the-art baselines across Hit Ratio (H@6), normalized discounted cumulative gain (NDCG@6), mean average precision (MAP@6), and AUC—sometimes by 15–25% relative gain in core metrics.
• Scaling Law: Empirical scaling studies on models with up to 0.1B parameters demonstrate strictly monotonic improvement as model size increases, consistent with the theoretical result that generator-only models can arbitrarily approach the oracle policy.
• Online A/B Tests: On a short-video recommendation platform serving over 500 million users, GoalRank improved key business metrics such as App Stay Time, Watch Time, and Effective View Count. Even small (e.g., 1.2%) uplifts on such metrics are impactful at operational scale.

A summary of the comparison between paradigms is presented below:

Paradigm	Model Structure	Main Limitation	Scaling Property
Generator–Evaluator (MG-E)	Two-stage	Saturating gains; cross-stage inconsistency	No scaling with k
GoalRank (Generator-only)	One-stage, large	Less business/contextual flexibility (see below)	Scaling law: error $\to$ 0 with size

* The one-stage approach may require further integration for dynamic or context-specific business signals.

5. Practical Implementation and Limitations

The generator-only GoalRank model is implemented as an end-to-end neural ranker, trained using the cross-entropy loss against the group-relative reference policy over mini-batches of candidate lists. Model scaling is achieved by progressively increasing neural capacity (width, depth, or parameter count). Input features and architectures (e.g., attention mechanisms, context encoding) can be adapted as needed.

Key practical advantages include:

• Eliminating the need for cross-stage optimization between generator and evaluator.
• Lower latency—only one forward pass is needed to generate a ranked list.
• Simpler model maintenance and deployment, as only a single, monolithic ranker needs to be versioned.

Noted limitations are:

• Reduced flexibility for rapidly adapting to shifting business goals or for incorporating diverse, context-dependent constraints, which can sometimes be handled more modularly in staged pipelines.
• Theoretical analyses require the reward model to provide reliable, relatively unbiased guidance for group-relative normalization, which may be challenging in dynamic user contexts.

6. Broader Impact and Applications

GoalRank is applicable to recommendation systems in short-video apps, e-commerce, and streaming services where listwise ranking accuracy determines user engagement and monetization. The methodology is suitable for settings where the candidate space is combinatorially large, feedback is abundant, and latency constraints preclude multi-stage rescoring.

A plausible implication is that as model sizes grow—leveraging distributed training and foundation-model-style architectures—generator-only systems like GoalRank are poised to supplant classical staged pipelines, provided that reward models (and data curation strategies) continue to improve.

7. Outlook

GoalRank establishes both the theoretical and practical foundation for generator-only ranking systems, emphasizing the significance of scalable capacity and principled objectives based on user feedback and robust normalization. Ongoing work is likely to focus on:

• Incorporating business-specific objectives and dynamic context into the generator-only paradigm.
• Better integrating retrieval and ranking modules end-to-end.
• Extending group-relative training methodologies for multi-objective or multi-interest recommendation scenarios.

The framework’s demonstrated scaling law suggests that investiture in larger generator models proportionally diminishes approximation error to the oracle policy, making it a preferred approach for industrial-scale ranking deployments (Zhang et al., 26 Sep 2025).