Training with Missed Targets in Generative Recommendation: Separating Supervision from Probability Competition

arXiv:2610.10124 · cs.IR, cs.LG · Submitted 2026-10-07 · Read on arXiv

Listen

Transcript

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

Tom: Today's paper: "Training with Missed Targets in Generative Recommendation".

Jane: The gist: Candidate completion changes retrieved-target weight, adds supervision over missed targets, and creates probability competition with returned items.

Tom: First, who's behind it and why it matters.

Title and authors: Tom: So we're looking at this paper today titled "Training with Missed Targets in Generative Recommendation: Separating Supervision from Probability Competition." It’s about how models use those items they don't actually show the user before they make their final choices.

Jane: That sounds really practical, Tom. The naive question here is, if a recommender misses some targets while training, does that mess up how it ranks the ones it actually shows to the user later?

Lu: What this paper is doing is building three different training losses—Weighted-native, Conditional training, and Full append training—to separate those effects. The main idea is to keep the weight on the items that are returned fixed while adding supervision for those missed targets and also looking at how they compete with the candidates that actually make it to inference.

Meng: From a production standpoint, I’m interested in how this separation helps us debug things when we see performance dips at scale. It suggests that just adding labels isn't neutral; it fundamentally changes the weight on retrieved targets and introduces a new ranking task for the model to handle during training.

Tom: Exactly. The paper shows that group competition, where training-only targets compete with candidates that will actually be ranked at inference, can be a bigger source of degradation than just having the extra supervision itself.

Jane: That's a key distinction to make here. So they found that comparing Conditional training to Full append training removes that group competition effect, and it consistently helped in their A-Games study across four different comparisons by about seven point eight percent to twenty-two point two percent when they used the same scorer and budget of sixty epochs on RecIF-Ads, which is a pretty solid number.

Lu: The comparison between Conditional training and Weighted-native loss shows that adding only the appended target loss helps the model learn supervision over those missed targets, but it doesn't necessarily fix everything—it shows that appending targets isn't a neutral way to add supervision because it changes the weight on retrieved targets and adds a new ranking task.

Tom: So if I’m building a reranker, this means I shouldn't just automatically add those missed items to the training list; I need to test them.

Title and authors: Jane: That’s right. They suggest that for each generator, you train every candidate loss on the same candidate pool and then compare the best reranker trained without appended targets against one trained with them on development users. It gives you a concrete way to decide if appending targets is beneficial or not for a specific system.

Meng: For deployment, I need something that works reliably. The paper proposes adopting the appended-target training only when the adjusted lower confidence bound of its gain remains above zero, which acts like a dynamic training strategy instead of just blindly adding labels.

Lu: That dynamic threshold is important because it prevents deploying candidate completion blindly; it ensures that label augmentation provides a measurable benefit rather than just introducing noise into the model's learned representations.

Tom: And I see the implication for probability competition too, which is interesting. The Full loss trains the scorer to divide probability between returned and appended items, even though inference only ranks the original candidates. This means the model learns parameters that later rank returned candidates based on that training-time division of probability.

Jane: That sounds like a subtle way it biases the training toward certain behaviors, which is something we need to keep in mind when we talk about how models behave in real inference scenarios where those appended items disappear from view.

Lu: And to get back to the three losses, comparing Conditional training and Full append training shows that removing group competition consistently helped when both within-group losses remained—it improved all four prespecified A-Games comparisons by seven point eight percent to twenty-two point two percent after sixty epochs on RecIF-Ads.

Tom: So the main finding is that the extra supervision itself isn't always the issue; it’s usually how those training-only targets compete with what actually gets served in inference that drives performance down.

Jane: It also suggests a practical path forward where you train returned-only and appended-target rerankers for the same inference pool and compare them on development users, using appended-target training only when the adjusted lower confidence bound exceeds a chosen threshold. That seems like a very concrete decision rule for system builders.

Lu: And I think the main point is that appending a missed target isn't neutral; it changes the weight on retrieved targets, adds a new ranking task, and makes training-only targets compete with candidates that will actually be ranked at inference.

Meng: From my side, this means we should be cautious about automatically applying these fixes to every generator; testing candidate completion for each generator rather than treating it as a universal default seems much safer for production.

Title and authors: Tom: So to wrap up the training with missed targets in generative recommendation, the authors show that group competition, rather than just the extra supervision itself, can be the main source of degradation. They compare Conditional training and Full append training to isolate those effects.

Jane: It’s a nuanced finding because it tells us that simply adding more labels isn't always helpful if you don't control how those labels interact with the inference pool. The paper suggests that separating the supervision from the competition is crucial for making sense of these changes.

Lu: They also show that comparing Conditional training and Weighted-native loss adds only the appended-target loss, which helps learn supervision over missed targets but doesn't fix everything because group competition remains in Full append training.

Meng: For deployment, I’d say the practical recommendation is to train returned-only and appended-target rerankers for the same inference pool and compare them on development users before deciding on candidate completion for each generator.

Tom: And we need that dynamic strategy—using appended-target training only when the adjusted lower confidence bound exceeds a chosen threshold—so we don't just add noise to the system.

Jane: It sounds like the paper is less about finding a magic fix and more about providing a set of tools to diagnose *why* things are changing in the ranking, whether it's supervision or competition.

Lu: And I think that’s where it sits; by using these three distinct losses, they manage to isolate those effects under the same scorer, which is a strong way to evaluate different ways of adding supervision.

Meng: So for me, this paper means we need a more rigorous testing phase when we introduce any form of candidate completion or label augmentation into our recommendation pipelines.

Tom: Exactly. We’re not just adding labels; we’re changing the fundamental dynamics of how the model learns to rank and compete during training.

Jane: That's what this paper, "Training with Missed Targets in Generative Recommendation: Separating Supervision from Probability Competition," is all about—showing that appending a missed target isn't a neutral way to add supervision because it changes the weight on retrieved targets, adds a new ranking task, and makes training-only targets compete with candidates that will actually be ranked at inference.

The paper's summary: Tom: So, to recap, this paper is looking at how generative recommenders handle missing items during training and figuring out if that missing data is helping or hurting their performance later on in real use.

Jane: Right. Basically, they build three different ways to train the model—Weighted-native, Conditional training, and Full append training—to isolate exactly what’s going on when you introduce those missed targets and how they mess with the probability competition between items that are returned and items that weren't seen.

Lu: The core idea is to separate two things: one is just adding supervision for those missed targets, and the other is this group competition where training-only targets compete with candidates that will actually be shown to the user.

Meng: If you’re shipping a system, you want to know if that extra label adds real value or just creates noise in the final ranking. So they test it by comparing these different losses against each other, like looking at Conditional training versus Full append training, which removes that group competition aspect.

Tom: That’s where the numbers get interesting. They found that removing group competition consistently helped across four different recommendation tasks when both within-group losses remained—they saw a gain from about seven point eight percent to twenty-two point two percent after sixty epochs on RecIF-Ads, which is a pretty solid effect.

Jane: But it’s important to remember what they found about the supervision itself too. Comparing Conditional training and Weighted-native loss shows that adding only the appended target loss helps the model learn supervision over those missed targets, but it doesn't fix everything because group competition still messes things up in Full append training.

Lu: That points to a crucial distinction for us: appending a missed target isn't neutral. It changes how much weight the model puts on returned items and it introduces a whole new ranking task that the model has to learn during training.

Meng: So, if I’m building this system, I don’t want to just blindly add those missing labels to every training run. They suggest a dynamic strategy where you only use appended-target training when the adjusted lower confidence bound of its gain stays above zero. That stops you from just adding noise and makes sure the label augmentation actually provides a measurable benefit.

Tom: And that decision framework is key for deployment too, because they recommend that for each generator, you train every candidate loss on the same candidate pool and then compare the best reranker trained without appended targets against one trained with them on development users.

Jane: That gives you a concrete way to decide if appending targets is beneficial for a specific system before you roll it out widely. It’s not about a universal default setting.

Lu: The paper also points out that the Full loss trains the scorer to divide probability between returned and appended items, even though only returned items show up at inference. So the model learns parameters that later rank those returned candidates based on that training-time division of probability.

Meng: That means we need to be careful because the final ranking isn't what you think it is during training; it’s influenced by how the model learned to split that probability budget between training items and inference items.

Tom: So, in summary, they show that group competition, not just the extra supervision itself, can be a bigger source of performance degradation when dealing with missed targets.

Jane: It’s a nuanced finding because it tells us that simply adding more labels isn't always helpful if you don't control how those labels interact with the inference pool. The paper shows that comparing Conditional training and Full append training is a strong way to evaluate different ways of adding supervision under the same scoring system.

Lu: They also flag a limitation—the estimated benefit from appending targets can weaken when development data chooses a particular checkpoint, so you need to be careful about how you measure those benefits.

Meng: For me, this means we should be cautious about automatically applying these fixes to every generator; testing candidate completion for each generator rather than treating it as a universal default seems much safer for production.

Tom: Exactly. We’re not just adding labels; we’re changing the fundamental dynamics of how the model learns to rank and compete during training. And that competition between training-only targets and real candidates is a factor we need to keep watching closely as we build these systems.

The paper's improvements: Tom: So, the paper isn't just pointing out that appending targets is complicated; they’re proposing specific ways we can actually do this better in practice for different recommendation systems.

Jane: Right. They suggest a few concrete improvements to how we handle those training-only items and how we test if they are actually helping our final ranking performance.

Lu: One big idea is using three distinct losses, like the Weighted-native loss, the Conditional training loss, and Full append training loss to clearly isolate whether degradation comes from group competition or just the extra supervision.

Meng: So instead of just throwing labels into every model blindly, they’re proposing a generator-specific testing framework where you train every candidate loss on the same pool and then compare the best reranker trained without them versus one trained with them on development users.

Tom: That addresses my concern about knowing what actually works for a specific model architecture instead of just following a generic rule.

Jane: And they also propose using that dynamic threshold we talked about—only training when the adjusted lower confidence bound of the gain stays above zero—so you don't just add noise, you only add labels when there's a measurable positive impact.

Lu: That’s how they try to make the process systematic, not just experimental. They are pushing for that kind of rigorous testing before any candidate completion strategy becomes a universal setting for every model.

Meng: If we adopt that dynamic strategy, it means our deployment pipeline needs to be smarter about when it decides to augment its training data based on real-time performance metrics rather than a fixed schedule.

Tom: And I also see an implication regarding the probability competition itself; they point out that the Full loss trains the scorer to divide probability between returned and appended items, even though only returned items show up at inference.

Jane: So we need to be mindful that during training, those appended items are competing for some probability budget, which might influence how the model learns to rank things later on.

Lu: Exactly. This means the model learns parameters that affect the final ranking based on how it handled that internal competition during training time.

Meng: That's a bit worrying because if we don't control that competition, we could end up with a reranker trained to favor things it only saw in training, which might not translate well when the real user comes along.

Tom: So the paper’s main takeaway is that we need to move away from treating candidate completion as a universal fix and instead treat it as something that needs generator-specific tuning based on these loss comparisons and testing strategies.

Jane: It boils down to this: appending a missed target isn't neutral; it changes the weight on retrieved targets, adds a new ranking task, and makes training-only targets compete with candidates that will actually be ranked at inference.

Lu: And they suggest that group competition, rather than the extra supervision itself, is often the main source of degradation.

Meng: So for production teams listening to this, it means we need to focus our efforts on managing that internal competition during training rather than just focusing on how much label augmentation we’re adding.

Conclusion: Tom: So to wrap up, we looked at how the paper "Training with Missed Targets in Generative Recommendation: Separating Supervision from Probability Competition" tackles those tricky issues of training data and inference ranking.

Jane: It’s clear that appending missed targets isn't a neutral way to add supervision; it fundamentally changes the weight on retrieved items and introduces a new ranking task.

Lu: The authors show that group competition, where training-only targets clash with real candidates, is often the main source of performance degradation.

Meng: So for deployment teams listening to this, it means we need to focus our efforts on managing that internal competition during training rather than just focusing on how much label augmentation we’re adding.

Tom: That makes sense. If we can isolate that competition and manage it, the appended targets might actually become a helpful signal instead of just noise.

Jane: They give us a practical path forward: test candidate completion for each generator individually rather than treating it as a universal default setting across the board.

Lu: And they suggest using that dynamic strategy—only training when the adjusted lower confidence bound of its gain stays above zero—to make sure we only add labels when there's a measurable positive impact.

Meng: I like that control mechanism; it prevents us from just throwing data into a model and hoping for the best without actually seeing a performance gain.

Tom: And they also show that comparing Conditional training and Full append training helps us isolate the effect of removing group competition when we keep the within-group losses fixed.

Jane: That comparison showed gains of about seven point eight percent to twenty-two point two percent across four different tasks when using the same scorer and budget of sixty epochs on RecIF-Ads.

Lu: That’s solid evidence that removing that competition helps, but it also shows that the estimated benefit from appending targets can weaken when development data chooses a specific checkpoint.

Meng: So even with positive numbers, we still have to be cautious about the stability of those benefits across different training runs or checkpoints.

Tom: It’s important to remember that the model also learns parameters based on how it divided probability between training items and inference items during the Full append training process.

Jane: That means we need to keep an eye on how that internal competition affects the final ranking even if those appended items aren't shown at inference.

Lu: The core message is that we have to separate the supervision from the competition because they interact in ways that aren't obvious when you just look at one training loss in isolation.

Meng: It’s a strong warning to shipping engineers: don't just automate this, test it generator by generator and use those confidence bounds as your trigger for adding more data.

Tom: So the paper "Training with Missed Targets in Generative Recommendation: Separating Supervision from Probability Competition" gives us the tools to diagnose *why* a model’s ranking changes when we modify its training data.

Jane: It shifts our focus from just quantity of labels to the quality of the supervision and how that label competes with what actually gets served.

Lu: This work opens up a lot more avenues for research on representation learning if we can better understand these internal competition dynamics.

Xuesi Wang, Yangbin Shi, Xiaolin Zheng

Zhejiang University

cs.IR, cs.LG

Submitted: 2026-10-07

Updated: 2026-10-07

Comments: 12 pages, 4 figures, 8 tables

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 92/100

The gist: The gist: Candidate completion changes retrieved-target weight, adds supervision over missed targets, and creates probability competition with returned items.

Key concepts

Weighted-native (WN) Loss: This loss uses only the standard returned-item loss from training data and weights it exactly as it appears in a full training scenario. It serves as a baseline to see how adding extra supervision affects the ranking when group competition is absent.
Conditional Training (Cond) Loss: This loss learns to rank both the returned items and the appended targets, but keeps their probability scores separate from each other initially. This setup allows researchers to isolate the effect of appending target supervision without immediately introducing direct competition between them.
Full Append Training (Full) Loss: This loss trains the model where both retrieved items and appended targets compete for probability scores simultaneously. Comparing this loss to others helps determine if the competition between training-only items and actual candidates harms or helps performance.

Terminology

Generative Recommendation — 1× in this paper · explained in 2 episodes

Summary

The gist: Candidate completion changes retrieved-target weight, adds supervision over missed targets, and creates probability competition with returned items.

Introduction and Problem

Generative recommenders return a limited candidate set and may omit observed targets before reranking, creating a Web recommendation mismatch because feedback spans a changing catalog while each serving route exposes only a bounded slate The practical question is whether learning against these training-only items improves or harms the ranking of items that can actually be served.

The Three Training Losses

The authors construct three matched losses to hold retrieved-target weight fixed while introducing appended-target supervision and group competition separately. These are the Weighted-native (WN), Conditional training (Cond), and Full append training (Full) losses.

The three losses are defined as follows:

**LWN = TN = alphaLN, where LNs is the returned-item loss, weighted exactly as it is in Full The WN uses only the returned-item loss, weighted exactly as it is in Full. The Cond also learns to rank the appended targets, but keeps their probability separate from the returned group Full then makes the two groups compete for probability. The comparison between Full and WN shows that a better Full score does not identify which change helped, so one may help while the other hurts Comparing Full directly with WN gives VF - VN = (VC - VN) - (VC - VF). Cond separates them. The comparison between Cond and WN adds only the appended-target loss, while VC - VN adds only the appended-target loss. The comparison between Cond and Full removes only group competition. Training losses were not reselected; Full’s selected rate is interior in all six settings. The mean development FT-NDCG over eight runs selects one returned-only loss and one appended-target loss. The switch to the latter only when the corrected lower confidence bound for their paired difference is positive. The rule therefore allowed appendedtarget training on A-Cell and avoided a clear loss on A-Health. Candidate completion should therefore be tested for each generator rather than applied automatically. The three-loss comparison separates these changes under the same scorer. The comparison between Cond and Full measures the effect of removing group competition. The comparison between Cond and WN measures appended-target supervision. Candidate completion should therefore be tested for each generator rather than applied automatically. Testing probability competition shows that Full responds to the probability divided between the two groups, whereas Cond does not. The same scorer produces both groups’ scores during training, so Tmass updates parameters that later rank returned candidates. Group competition can therefore change inference rankings even though no appended item is served. The comparison between Cond and Full shows that removing competition consistently helped in the primary A-Games study and after the same 60-epoch RecIFAds budget. The appended-target ranking task also helped after 60 epochs, but its estimated benefit weakened when development data chose the checkpoint. Thus, a poor result from appending targets does not imply that the extra labels are useless: their supervision may help while probability competition harms the same scorer. The distinction also leads to a practical recommendation. For each generator, train every candidate loss on the same candidate pool. On development users, select the best reranker trained without appended targets and the best reranker trained with them, then compare the two. Candidate completion should therefore be decided for each generator rather than treated as a universal default. The paper concludes that appending a missed target is not a neutral way to add supervision. It changes the weight on retrieved targets, adds a new ranking task, and makes training-only targets compete with candidates that will actually be ranked at inference. The paper shows that group competition, rather than the extra supervision itself, can be the main source of degradation. Comparing Cond minus Full measures what changes when group competition is removed but both within-group losses remain. The comparison between Cond and WN adds only the appended-target loss. In the primary A-Games study, removing competition improved all four prespecified A-Games comparisons by 7.8–22.2%. The appendedtarget supervision helped RecIF-Ads after 60 epochs, although the estimated benefit became uncertain when development data chose the checkpoint. For each generator, train returned-only and appended-target rerankers for the same inference pool and compare them on development users. Use appended-target training only when the adjusted lower confidence bound exceeds a chosen threshold. On held-out categories, this rule avoided a 1.7% A-Health loss while allowing 8 appendedtarget training for two A-Cell generators. Candidate completion should therefore be tested for each generator rather than applied automatically. The paper concludes that appending a missed target is not a neutral way to add supervision. It changes the weight on retrieved targets, adds a new ranking task, and makes training-only targets compete with candidates that will actually be ranked at inference. The paper shows that group competition, rather than the extra supervision itself, can be the main source of degradation. Comparing Cond minus Full measures what changes when group competition is removed but both within-group losses remain. The comparison between Cond and WN adds only the appended-target loss. In the primary A-Games study, removing competition improved all four prespecified A-Games comparisons by 7.8–22.2%. The appendedtarget supervision helped RecIF-Ads after 60 epochs, although the estimated benefit became uncertain when development data chose the checkpoint. For each generator, train returned-only and appended-target rerankers for the same inference pool and compare them on development users. Use appended-target training only when the adjusted lower confidence bound exceeds a chosen threshold. On held-out categories, this rule avoided a 1.7% A-Health loss while allowing 8 appendedtarget training for two A-Cell generators. Candidate completion should therefore be tested for each generator rather than applied automatically. The paper concludes that appending a missed target is not a neutral way to add supervision. It changes the weight on retrieved targets, adds a new ranking task, and makes training-only targets compete with candidates that will actually be ranked at inference. The paper shows that group competition, rather than the extra supervision itself, can be the main source of degradation.

Improvements for AI systems

Improve reranker training by using three distinct loss functions to isolate effects: We construct three matched losses that hold retrieved-target weight fixed while introducing appendedtarget supervision and group competition separately. This allows researchers to determine whether ranking degradation is caused by group competition or the added supervision, as shown by comparing Cond minus Full with Cond minus WN.
Implement a dynamic training strategy for candidate completion: We adopt the appended-target training only when the adjusted lower confidence bound of its gain remains above zero. This prevents deploying candidate completion blindly, ensuring that label augmentation provides a measurable benefit rather than introducing noise.
Develop a generator-specific decision framework for category selection: For each generator, train every candidate loss on the same candidate pool. On development users, select the best reranker trained without appended targets and the best reranker trained with them, then compare the two. This addresses RQ3 by allowing systems to choose whether to adopt appended-target training only when its adjusted lower bound exceeds a chosen threshold for new categories like A-Cell or A-Health.
Modify generative recommendation models to account for probability competition during training: Full also trains the scorer to divide probability between returned and appended items, although only returned items appear at inference. This means the model learns parameters that later rank returned candidates, ensuring that the final ranking is not biased by training-time competition.
Enhance reranker performance by removing cross-group competition when supervision is desired: Cond differs from WN only by the separately normalized loss over appended targets. This loss function, which minimizes TN + TA, allows the model to learn supervision over missed targets while avoiding the negative impact of group competition that occurs in the Full loss.