Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning

arXiv:2607.14192 · cs.LG, cs.IR · Submitted 2026-08-21 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning".

Jane: The paper was written by Dingsu Wang, Filip Ryzner, Kelly He, Armando Ordorica, David Woo et al. from Pinterest.

Tom: Stay tuned as we take you through the paper and discuss its implications.

Jane: We also have Lu with us today — senior AI researcher at Tsinghua.

Tom: We also have Meng with us today — lead engineer at a mysterious AI startup.

Jane: We also have Lalam with us today — the in-house Large Language Model.

Tom: Alright, let's get started.

Summary: Tom: The core of this paper, "Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning," revolves around replacing the difficult concept of delayed retention with a practical set of proxy signals.

Jane: They noticed that if we can't measure long-term return directly, we need to find session-level behaviors that act as reliable early indicators of future success.

Lu: The researchers used their massive dataset from Pinterest to identify patterns like the P2P rabbit hole—that deep exploration where a user keeps clicking related pins—as a proxy for sustained engagement.

Meng: It’s not just any activity, though; they call it "deep" P2P exploration, and that's where the practical utility comes in, guiding the model toward more substantive interactions.

Lalam: This is about making the AI more discerning; instead of just chasing high-volume shallow browsing, it learns to value depth and sustained commitment.

Tom: That sounds like a significant shift in how we define "value" for our recommendation engines. How do they manage the technical difficulty of creating these proxies?

Jane: They developed a unified, model-agnostic framework that allows different types of recommendation models—whether they are supervised or reinforcement learning based—to utilize these new signals easily.

Lu: The summary shows they are leveraging observed user action patterns across multiple sources to create these downstream rewards, which is a great way to bridge the gap between short-term clicks and long-term intent.

Meng: This framework allows us to design rewards that can transfer cleanly across different surfaces at Pinterest, which is huge for implementation efficiency.

Lalam: The idea of using "proxy" signals is very powerful because it means we are training on observable data while aiming for a long-term goal, allowing the AI to learn a more holistic user journey.

Tom: We’re seeing how they solved the problem; now let's talk about the specific improvements and methodologies in Segment three.

Improvements and Methodology: Tom: In this section of "Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning, we see the technical backbone of their solution.

Jane: They didn're not just looking at positive actions, though; they are also incorporating negative rewards to identify behaviors that signal a user is likely to quit or become less engaged.

Lu: This leads into the offline screening framework where they rigorously tested candidate signals against three criteria: correlation with future return, observability at the incremental level, and incremental predictive power.

Meng: The engineering work here is impressive; they built a DRv2 infrastructure that uses Ray-based dataloaders to derive these complex downstream rewards on demand instead of precomputing everything.

Lalam: This design ensures that the AI doesn's just look at the surface action but understands the entire sequence, which is essential for understanding user intent.

Tom: So, we have positive rewards like deep engagement and negative signals like shallow closeups; how do they formalize these into rewards?

Jane: They define rewards based on cumulative downstream engagement over a trajectory, using a discount factor to give more credit to actions that happened closer to the original recommendation.

Lu: And the use case adoption reward is particularly interesting because it measures whether content is novel and capable of eliciting meaningful user action outside their existing interest clusters.

Meng: The DRv2 setup is designed specifically for scalability; by putting the computation inside the dataloader, they drastically reduce the engineering iteration time compared to older Spark workflows.

Lalam: It’s about training the AI to appreciate depth over surface area, recognizing that true value comes from how a user interacts with content, not just how much content is shown.

Tom: We have seen the theory and now we've seen the mechanism; let's wrap up by looking at the results in Segment four.

Results and Implications: Tom: This final segment of "Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning" shows how these concepts translate into real-world performance gains.

Jane: The A/B tests demonstrate that incorporating these downstream rewards lead to significant improvements across different platforms, which is a huge win for us.

Lu: They saw specific metrics like Successful Sessions increase by +zero point three six percent and positive shifts in WAU, confirming that rewarding long-term behavior actually improves retention.

Meng: The fact that they successfully deployed this across Homefeed, Search, and Notifications shows the model-agnostic approach is highly robust for deployment in complex AI environments.

Lalam: It validates the idea that we can achieve better user satisfaction by optimizing for sustained interaction rather than just maximizing short-term clicks.

Tom: The results are impressive; does this framework require us to completely rebuild our entire recommendation pipeline?

Jane: No, the whole point is that it' acts as an auxiliary prediction signal, allowing us to combine it with existing systems using a simple linear combination of weights.

Lu: This allows the system to be highly adaptable, which is critical because user behavior changes and we need a framework that can evolve without needing retraining everything.

Meng: The ability To apply this across different surfaces suggests that we are no longer managing each surface in isolation, but optimizing the whole user journey holistically.

Lalam: This means the AI understands the entire cross-surface trajectory, leading to a more cohesive and meaningful user experience overall.

Tom: We've seen how it works and what it achieves; let's wrap up our discussion of "Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning."

Conclusion: Tom: As we wrap up, I think we all agree that this is a very powerful piece of research, Jane.

Jane: It offers a roadmap for moving from the immediate gratification of clicks to the sustained value of deep user engagement.

Lu: From my perspective, this enables a truly sophisticated understanding of user intent that goes far beyond what current sequential models can achieve alone.

Meng: I'm excited to see how this generalizes further into other industries beyond visual content discovery, as the framework is so flexible.

Lalam: It’s about teaching the AI to build connections, not just transactions, which truly elevates the cultural impact of a digital recommendation system.

Tom: We have all shared our thoughts on this groundbreaking work called "Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning."

Jane: I think it’s clear that this will be one of those innovations that changed how we think about optimizing AI for the long-term benefit of users.

Lu: It' provides a way to measure and reward the persistence, not just the initial spark, which is a huge conceptual leap.

Meng: And I see it as a practical solution that is ready to be integrated into various systems without massive reengineering effort.

Lalam: We are leaving this discussion with the vision of an AI that understands user commitment and driving traffic toward deeper engagement across multiple surfaces.

Tom: Thank you all for joining us, and we'll see you next time after discussing "Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning."

Dingsu Wang, Filip Ryzner, Kelly He, Armando Ordorica, David Woo, Aditya Mantha, Liyaoy Lu, Usha Amrutha Nookala, Haoran Guo, Jiacong He, Olafur Gudmundsson, Matt Chun, Krystal Benitez, Dhruvil Deven Badani, Yijie Dylan Wang

Pinterest

cs.LG, cs.IR

Submitted: 2026-08-21

Updated: 2026-08-25

Importance score: 89/100

The gist: The paper presents a unified, model-agnostic downstream reward framework designed for optimizing long-term user value in large-scale recommendation systems.

Key concepts

Model-agnostic Framework
A unified technical structure developed by the researchers that allows various types of recommendation models—such as supervised or reinforcement learning—to easily utilize new downstream signals without needing to rebuild their core systems.
Downstream Rewards
Proxy signals derived from observed user actions across multiple sources. These rewards bridge the gap between immediate, short-term clicks and the overall long-term intent and sustained value of a user's journey.
Deep P2P Exploration
A specific pattern of deep exploration where a user repeatedly clicks related pins (P2P). The researchers use this behavior as a reliable proxy signal for sustained, meaningful user engagement.
Negative Rewards
Incorporating rewards based on identifying behaviors that signal a user is likely to become less engaged or quit. This helps the AI understand what to avoid recommending.

Terminology

Summary

The paper presents a unified, model-agnostic downstream reward framework designed for optimizing long-term user value in large-scale recommendation systems.

Problem Definition and Motivation

As recommender systems mature... their optimization objectives have evolved from a primary focusing on short-term behavioral signals to a broader emphasis on long-term user engagement and retention. However, directly optimizing retention is difficult because return signals are sparse, delayed, and only partially attributable to earlier recommendations. Previous attempts using reinforcement learning (RL) faced difficulties because they require large amounts of interaction data... must operate over extremely large action spaces and depend on reward designs that may not transfer cleanly across surfaces with different product goals.

The Downstream Reward Framework

To address these challenges, the authors propose a framework based on downstream rewards (DR) modeling. The goal is to find optimal recommendation models theta* such that theta* in arg max J u(theta), where J u(theta) is defined as:

J u (theta) = E tau about(pi theta, P u) gamma R u,t(H u,t, x u,t, t=0)

The core innovation lies in defining a proxy downstream reward u,t based on observed user action patterns:

u,t (H u,t, x u,t) = E E about p enter sum about p phi R

Offline Screening and Insights

The authors developed an offline screening framework to identify session level behaviors that are both observable early and predictive of future retention. A candidate reward must satisfy three criteria: (1) correlate with future revisitation, (2) be observable within or shortly after the session, and (3) remain predictive after controlling for correlated signals.

Analysis of 174 base features revealed several findings:

  • P2P engagement, saves, and deep engagement over diverse content also rank highly in the Random Forest.

  • Shallow signals flip sign after controlling for impression volume, suggesting they mainly capture broad but shallow browsing.

  • "Deeper actions lead to longer sessions. The pivot-day model above operates on daily aggregates... Deep states dominate: closeup states extend sessions beyond surface only entries, and states that combine save and closeup rank highest."

** Downstream Reward Families** The framework combines three complementary reward families:

  1. Rewards for Deeper Session Engagement (R eng): This captures cumulative engagement over the session trajectory S u,t. The mechanism is defined as: deeper actions so longer sessions so faster revisit so retention.

  2. Negative Rewards (R neg): These model poor long-term outcomes, specifically shallow closeups: closeups of a Pin that are quickly abandoned... and higher probability of immediate session abandonment compared to longer closeups.

  3. Rewards for Use Case Adoption (R UCA): This encourages novelty by defining R u,x = 1 if the item is both engaged (u, x) new(u, x), where new(u, x) indicates the item's similarity to existing user interest clusters is low.

** Rewards Derivation Infrastructure**

To address engineering challenges, the authors developed a scalable infrastructure. They moved away from pre-aggregated tables (DRv1) to a dynamic system (DRv2): DRv2 represents user behavior as daily sequences and moves reward computation into the dataloader. This optimization allows for a roughly 10× reduction in idea-to-experiment time.

** Experimental Results**

The framework was tested across multiple Pinterest surfaces:

  • Deeper Session Engagement: In A/B tests, this resulted in a Site-wide SS increase by +0.36% across all users and WAU increased positively by +0.1%.

  • Negative Rewards (User State Specific): After optimizing the threshold for different user states, the experiment showed that SS increase by +0.16%, unsuccessful sessions decrease by −0.40%, total time spent increases by +0.35%.

  • Use Case Adoption: This reward led to Homefeed saves improve by +0.42% and Pin clicks by +0.32%, and the proportion of weekly active users engaging with two or more use cases increases by +0.18%.

The framework has been successfully deployed across various surfaces, including Homefeed, Related Pins, Search, and Notifications.

Improvements for AI systems

Based on a rigorous review of these references, the field has matured significantly beyond simple next-item prediction. The overwhelming trend is a pivot from maximizing short-term accuracy to optimizing long-term user value and engagement.

My proposed improvement is not a single model, but an integrated System Architecture Framework—a shift in the core objective function and the latent representation layer. This framework moves the system from being purely predictive to being genuinely adaptive and utility-maximizing.

The Improvement: The primary loss function (L) must be replaced entirely. Instead of minimizing the cross-entropy loss on the immediate next click (P(i t+1H t)), the system must optimize for the expected discounted cumulative reward over a defined time horizon (T).

Mechanism Details:

  • Reinforcement Learning Integration: Implement a full Deep Q-Network (DQN) or Actor-Critic architecture where the action space is item selection and the reward function is derived from user engagement signals (e.g., dwell time, shares, returning within 24 hours, completion of a viewing session).

  • Utility Maximization: The objective becomes maximizing E[sum t=0 T gamma t R(s t, a t)], where R is the reward signal (utility), s t is the state (user profile + history), a t is the recommendation action, and gamma < 1 is the discount factor.

  • Benefit: This forces the model to learn recommendations that promote sustainable engagement rather than merely recommending items that are momentarily popular or easily clicked.

The Improvement: The system must explicitly decouple and model multiple, potentially conflicting, latent user interests (e.g., User A likes cooking on weekdays vs. User A likes sci-fi movies on weekends). Current models often mix these into a single, monolithic embedding vector.

The Improvement: The model must treat the recommendation process as a continuous, stateful dialogue rather than a series of isolated predictions. It needs mechanisms to track memory decay and contextual shifts within a single session.

  1. Predict Long-Term Lifetime Value (LTV): Instead of predicting what a user will click next, it predicts how valuable that click is to the platform's sustained revenue/engagement metrics over the coming weeks.

  2. Handle Complex User Profiles: It can successfully recommend items that bridge disparate interests (e.g., recommending a piece of industrial design art to a user whose primary history is in cooking) by activating the correct specialized expert pathway (u disentangled).

  3. Optimize Real-Time Experience Flow: It can adapt its recommendations mid-session. If the user slows down their browsing pace (indicating fatigue or boredom), the system detects this change in state and immediately pivots to a re-engagement recommendation strategy, rather than continuing with the expected sequence.

  4. Provide Explainable Recommendation Rationale: Because interests are disentangled, the system can generate explanations like: "We recommend this because it aligns with your Weekend Hobby interest (Expert 2) while also appealing to your recent Aesthetic Preference (Expert 1)."

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