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From Valid to Useful: Post-Verification Acquisition for Recursive Self-Improving Recommendation

Tonmoy Hasan, Taylor Foust, Shao Tang, Leonardo Neves, Aman Gupta, Hiroto Udagawa, Helder Dias, Daniel Silva, Rohan Ramanath

arXiv:2610.04302v1cs.IR

TL;DR

Recursive self-improvement must decide not only which synthetic sequences pass fidelity verification, but also which verified sequences should train the successor model. DA-RSIR caps source contributions and selects verified candidates using posterior disagreement; across four datasets and three recommenders, it outperforms retain-all RSIR in all 24 retraining comparisons.

  • Problem

    Fidelity verification checks consistency with a source sequence, but retain-all gives more training influence to sources with more candidates or longer continuations without showing which candidates benefit successor training.

    Source: §1 · p. 2

  • Method

    DA-RSIR preserves generation and verification, caps each source’s contribution, and samples verified candidates across disagreement ranges using MC-dropout estimates of posterior disagreement.

    Source: §1 · p. 2

  • Results

    DA-RSIR outperforms retain-all RSIR in all 24 retraining comparisons, has the highest mean on both metrics, and one update exceeds retain-all’s best gain over five rounds.

    Source: §6 · p. 8

  • Takeaways & Limitations

    Post-verification acquisition is a separate control point that can affect both immediate performance and later recursive updates.

    Source: §6 · p. 8

    Evaluation is limited to offline ranking metrics on four sparse, short-history benchmarks with attention-based models; online evaluation remains future work.

    Source: §7 · pp. 8–9

Abstract

from arXiv · show

Sequential recommenders can generate synthetic interaction sequences and retrain on the augmented corpus in a recursive self-improvement loop. To limit error accumulation, current methods verify each generated sequence remains predictive of the user's real interactions and discard those that drift away from it. Verification does not, however, determine which verified sequences should train the next model. With every verified sequence used for training, source sequences yielding more verified sequences or longer continuations have more influence, although neither quantity indicates how much those sequences will help the next model. We formulate the decision of which verified sequences are used to train the next model as \emph{post-verification acquisition} and introduce {\bf Disagreement-Aware Recursive Self-Improving Recommendation (DA-RSIR)}. DA-RSIR caps each source sequence's contribution and ranks its verified sequences by how much the model's predictions disagree over their augmented interactions. It uses a score derived from Bayesian Active Learning by Disagreement (BALD) and estimated with Monte Carlo (MC) dropout. DA-RSIR requires no extra labels, teacher model, or quality scorer. Across four datasets, three recommender models, and two metrics, it improves on the retain-all approach in all $24$ comparisons and attains the highest mean in $23$ of $24$ overall; the aggregate improvement is statistically significant on both metrics. A single DA-RSIR round exceeds the retain-all approach's best gain over five recursive rounds. These findings establish post-verification acquisition as a separate control point in recursive self-improvement, separating which sequences pass verification from which verified sequences are used to train the next model.

1 Introduction

Recursive self-improvement can amplify errors, and fidelity verification alone does not decide which verified sequences should train the successor model. DA-RSIR makes that acquisition decision explicit, using posterior disagreement to select candidates; retraining improves on retain-all RSIR across all 24 model–dataset–metric comparisons.

1 Introduction

  • RSIR retains every verified sequence, so sources yielding more candidates or longer continuations contribute more training examples, although neither quantity indicates successor-model benefit.In first-round experiments, RSIR has higher mean Recall@10 than training only on real data in all 12 model–dataset settings, but lower mean NDCG@10 in four.Source: §1 · p. 2
  • DA-RSIR leaves generation and verification unchanged, limits each source sequence’s contribution, and samples verified candidates across disagreement ranges estimated with MC dropout.Its acquisition requires no additional labels, external teacher, or separately trained quality model.Source: §1 · p. 2
  • The paper frames post-verification acquisition as a distinct control problem: verification checks source consistency, while acquisition chooses which verified candidates train the next model.This separates passing fidelity verification from selection for successor training.Source: §1 · p. 2
  • DA-RSIR achieves a higher mean than retain-all RSIR in all 24 retraining model–dataset–metric comparisons.The evaluation covers four datasets and three recommender models.Source: §1 · p. 2

2 Related Work

Prior work generates or filters training examples and studies acquisition of informative examples, but DA-RSIR acts specifically on model-generated sequences that have passed source-conditioned fidelity verification. It uses posterior disagreement to guide that selection without assuming that the highest-disagreement candidates are always preferable.

Sequential recommendation and data augmentation

  • Data-augmentation methods generate examples through transformations or learned generators, whereas DA-RSIR selects among verified model-generated sequences for successor training.This places DA-RSIR after generation and verification rather than among methods that determine how examples are generated.Source: §2 · p. 2

Recursive self-improvement and self-consuming training

  • Filtering methods retain or remove generated supervision before training, but RSIR’s fidelity check still leaves selection among verified candidates unresolved.DA-RSIR leaves verification unchanged and makes post-verification selection explicit.Source: §2 · p. 3

Posterior disagreement and data acquisition

  • BALD scores examples by mutual information between predictions and model parameters, and Monte Carlo dropout approximates it in neural networks.Batch acquisition work warns that independently selecting top-scoring examples can yield redundant batches, motivating DA-RSIR’s avoidance of a top-score-only rule.Source: §2 · p. 3

3 Preliminaries and Problem Formulation

The section defines sequential prediction and recursive training, then separates fidelity verification from acquisition. It formulates acquisition as choosing a subset of verified candidates to improve real-interaction performance, while noting that direct optimization is impractical.

3.1 Sequential Recommendation

  • The initial real corpus consists of users’ chronologically ordered interaction sequences, and the recommender models a next-item distribution from preceding interactions.Training uses a next-item prediction objective.Source: §3.1 · p. 3

3.2 Recursive Self-Improvement with Fidelity Control

  • At each recursive round, the model trains on the real corpus plus accumulated acquired synthetic increments, and any current-corpus sequence may serve as a generation source.Generation makes up to a predefined number of attempts from each source.Source: §3.2 · p. 3
  • Generation proposes items using top-κ sampling, while fidelity verification accepts an extension only when an unused source item remains within the top τ predictions.If verification fails, generation stops and retains the last verified context; short candidates are removed and exact duplicates are deduplicated.Source: §3.2 · pp. 3–4

3.3 Verification and Acquisition

  • Acquisition operates only on the fidelity-valid candidate bank; RSIR uses retain-all, adding every candidate surviving verification and basic filtering.The acquired sequences are added to the successor corpus.Source: §3.3 · p. 4
  • Under retain-all, sources yielding more verified candidates or longer continuations contribute more training targets, though neither quantity indicates how much they help the successor model.Since acquired sequences may become sources in later rounds, different selections can affect both successor training and future source sequences.Source: §3.3 · p. 4

3.4 Post-Verification Acquisition Problem

  • The ideal acquisition subset would yield the best successor model on real interactions, but exhaustive subset search and successor-model training make direct optimization impractical.The paper therefore seeks a tractable policy that bounds each source’s contribution and organizes candidates using posterior disagreement.Source: §3.4 · p. 4

4 Disagreement-Aware Recursive Self-Improvement

DA-RSIR leaves generation and fidelity verification unchanged, then acquires verified candidates for successor training using disagreement-based ranking and sampling. Its acquisition procedure groups proposals, samples across disagreement ranges, and feeds selected sequences into recursive successor training.

4 Disagreement-Aware Recursive Self-Improvement

  • DA-RSIR preserves RSIR’s generation and fidelity verification, changing only which verified candidates are acquired for successor training.Acquisition ranks candidates within proposal groups by posterior disagreement and samples across low-, middle-, and high-disagreement ranges.Source: §4 · p. 4

4.1 Target-Specific Posterior Disagreement

  • MC dropout estimates posterior disagreement across generated positions, and DA-RSIR scores each generated target’s contribution rather than full sequence mutual information.The sequence-level score averages target-specific contributions; averaging prevents automatic growth with sequence length, although some length dependence may remain.Source: §4.1 · p. 5

4.2 Grouped Acquisition

  • Generation attempts are assigned to B disjoint proposal groups before scoring; candidates are ranked within each group and divided into disagreement regimes for representative sampling.Groups with at most three verified candidates retain all of them; larger groups sample one candidate uniformly from each of the low-, middle-, and high-disagreement regimes, limiting each source to at most 3B sequences.Source: §4.2 · pp. 5–6

4.3 Successor Training and Recursive Update

  • After acquisition, the successor is trained on the augmented corpus, and acquired sequences may serve as sources in the next recursive round.DA-RSIR retrains from a new random initialization, while DA-RSIR-FT fine-tunes the current model on the same augmented corpus; acquisition uses the current model and verified candidate bank without training a successor during selection.Source: §4.3 · p. 6

5 Experiments and Results

DA-RSIR leads retain-all RSIR across the first-round benchmark comparisons, while the recursive and fixed-count analyses examine how acquisition choices affect performance. The evaluation spans four datasets and three recommender models, with results reported across two metrics and multiple training settings.

5 Experiments and Results

  • The evaluation covers four datasets, three recommender models, retraining and fine-tuning, and single- and multi-round settings.Table 1 reports first-round means and sample standard deviations across four runs, with values multiplied by 100.Source: §5 · p. 6, Table 1 · p. 7

5.1 Overall Performance

  • 23 of 24 model–dataset–metric columns have a DA-RSIR variant with the highest mean, and DA-RSIR beats RSIR in all 24 retraining comparisons with p < 0.05.The comparisons use matched candidate-generation and fidelity-verification configurations; Table 1 reports first-round means and sample standard deviations over four runs.Source: Table 1 · p. 7, §5.1 · p. 7

CL4SRec

  • In first-round next-item results (mean ± sample SD over four runs, values ×100), Improvbest spans −1.8% to +2.5% versus the strongest baseline; Improvbase spans +1.3% to +11.3% versus Base.Improvbest and Improvbase report the best DA-RSIR variant’s relative gains over the strongest baseline and Base, respectively.Source: CL4SRec · p. 7, Table 1 · p. 7

FEARec

  • In first-round next-item recommendation results, Improvbest values range from +0.2% to +2.9% and Improvbase values from +0.9% to +12.2%, representing relative gains over the strongest baseline and Base, respectively.Source: FEARec · p. 7, Table 1 · p. 7

5.2 Recursive Performance and Disagreement-Based Selection

  • After one round, DA-RSIR improves over the initial model by 6.3% in NDCG@5 and 8.8% in Recall@5, exceeding RSIR’s best five-round gains.In the five-round single-seed analysis, DA-RSIR remains above RSIR for all five Recall@5 rounds and four of five NDCG@5 rounds; performance peaks at round 4 and declines at round 5.Source: §5.2 · p. 8, Figure 3 · p. 8
  • DA-RSIR has the highest mean on both metrics among fixed-count policies, while Middle-3 leads single-regime NDCG@10 and High-3 leads single-regime Recall@10.Policies use the same verified bank and acquire three candidates per proposal group; differences among fixed-count policies are modest relative to across-seed variability.Source: §5.2 · p. 8, Table 2 · p. 8

6 Conclusion

The conclusion frames post-verification acquisition as a distinct control point: verification decides which candidates pass, while acquisition decides which verified candidates train the next model. Experiments find that limiting the acquired increment contributes to improvement, and that DA-RSIR’s across-regime selection performs strongly across both metrics and recursive updates.

6 Conclusion

  • Across four datasets and three recommender models, DA-RSIR outperforms retain-all RSIR in all 24 retraining comparisons.DA-RSIR preserves RSIR’s generation and verification, limits each source sequence’s contribution, and samples verified candidates across disagreement ranges.Source: §6 · p. 8
  • Limiting the acquired increment contributes to improvement, and DA-RSIR attains the highest mean on both metrics despite no single disagreement regime dominating both.Within the five-round evaluation, one DA-RSIR update exceeds the best gain reached by retain-all RSIR; the conclusion states that post-verification selection affects immediate performance and later recursive updates.Source: §6 · p. 8

7 Limitation and Future Work

The section bounds the evaluation and identifies online evaluation and broader recommendation settings as future work. It also describes how the acquisition algorithm scores and selects verified candidates for the next synthetic increment.

7 Limitation and Future Work

  • Evaluation covers ID-based sequential recommendation, not text-based recommendation; long-history, session-based, industrial-scale, non-attention, language-model-based, and online settings remain future work.The reported benchmarks are sparse and short-history, and the evaluated models are attention-based.Source: §7 · pp. 8–9

A DA-RSIR Acquisition Algorithm

  • Algorithm 1 takes fidelity-valid candidates, the current model, MC samples, and an experiment seed, then returns the next synthetic increment.It scores candidates and selects representatives from disagreement regimes.Source: A DA-RSIR Acquisition Algorithm · p. 12
  • Within each proposal group, candidates are ordered by disagreement and posterior likelihood; groups with more than three candidates yield one sample from each low, middle, and high regime.Exact duplicate sequences are removed before returning the increment.Source: A DA-RSIR Acquisition Algorithm · p. 12

B Additional Experimental Details

The experiments use four preprocessed sequential-recommendation benchmarks, a leave-one-out evaluation protocol, and matched comparisons that vary the acquisition rule. The section also specifies shared tuning, DA-RSIR settings, and compute details.

B Additional Experimental Details

  • Four 5-core benchmarks—Amazon Toys, Amazon Beauty, Amazon Sports, and Yelp—are sparse, with average sequence lengths of 5.3–7.1 items.Users’ remaining interactions are ordered chronologically, and the same splits are used across methods.Source: B Additional Experimental Details · p. 12
  • Evaluation reserves each user’s last interaction for testing, the second-to-last for validation, and earlier interactions for training, reporting Recall@10 and NDCG@10 over four seeds.Held-out items are ranked against the full catalog; reported values are multiplied by 100.Source: B Additional Experimental Details · p. 12
  • RSIR and DA-RSIR share the inherited generation and verification settings, so each matched pair differs only in its post-verification acquisition rule.The fidelity threshold, local-candidate probability, top-κ cutoff, and minimum retained sequence length are tuned using validation NDCG@10.Source: B Additional Experimental Details · p. 12
  • The final DA-RSIR configuration uses B = 2, M = 10, and S = 10, while Figure 4 varies B at a fixed 12,897 acquired sequences.The selected B and M give 20 generation attempts per source sequence.Source: B Additional Experimental Details · p. 13, Figure 4 · p. 13
  • Experiments run on one node with eight NVIDIA H100 GPUs, and proposal-matched controls hold generation, fidelity decisions, deduplication, and successor training fixed.Only the post-verification acquisition rule differs across those controls.Source: B Additional Experimental Details · p. 13

C Additional Result Analyses

The additional analyses examine sensitivity to proposal-group count and find setting-dependent differences while holding acquisition volume fixed for the grouping comparison.

C Additional Result Analyses

C.1 Sensitivity to Proposal Grouping

  • At fixed 12,897 acquired sequences, B = 2 leads under retraining on both metrics; fine-tuning favors B = 2 for Recall@10 and B = 5 marginally for NDCG@10.Performance varies non-monotonically: all tested B values improve Recall@10 over Base, while retraining at B = 3 and B = 4 returns approximately to Base NDCG@10.Source: §C.1 · p. 13

C.2 Sensitivity to Monte Carlo Sample Count

The sensitivity analysis favors S = 10 across the reported metric and training-procedure combinations, while noting stability and a competitive lower-sample setting.

C.2 Sensitivity to Monte Carlo Sample Count

  • S = 10 attains the highest mean in all four metric and training-procedure combinations; Table 3 reports four-seed means ×100 for SASRec on Amazon Beauty.Source: §C.2 · pp. 13–14, Table 3 · p. 14
  • Performance remains stable across the tested sample-count range, and increasing S beyond 10 provides no further improvement.Source: §C.2 · p. 14
  • S = 5 remains competitive with half as many stochastic forward passes, though S = 10 is retained for its consistently highest means.Source: §C.2 · p. 14

D Dataset and Acquired-Corpus Statistics

In the first recursive round, DA-RSIR expands training corpora within the existing user and item sets, with the verified candidate yield varying by model.

D Dataset and Acquired-Corpus Statistics

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