arxiv: 2604.21536 · v1 · submitted 2026-04-23 · 💻 cs.IR · cs.AI

Recognition: unknown

Pre-trained LLMs Meet Sequential Recommenders: Efficient User-Centric Knowledge Distillation

Nikita Severin , Danil Kartushov , Vladislav Urzhumov , Vladislav Kulikov , Oksana Konovalova , Alexey Grishanov , Anton Klenitskiy , Artem Fatkulin

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Alexey Vasilev Andrey Savchenko Ilya Makarov

Authors on Pith no claims yet

Pith reviewed 2026-05-09 20:22 UTC · model grok-4.3

classification 💻 cs.IR cs.AI

keywords sequential recommender systemsknowledge distillationlarge language modelsuser profilesefficient inferencerecommender systems

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The pith

Knowledge from pre-trained LLMs can be distilled into sequential recommender systems to add semantic depth without slowing inference.

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

Sequential recommender systems model sequences of user interactions effectively but lack deeper understanding of user intent and semantics. Pre-trained large language models can generate rich textual user profiles that capture this missing context through reasoning. The paper shows how to distill those profiles into standard sequential models via knowledge distillation during training. This transfer happens once, after which the recommender runs exactly as before with no LLM calls at serving time. A reader would care because real-world recommenders must respond instantly to millions of users, making direct LLM integration impractical.

Core claim

The paper establishes a knowledge distillation pipeline that first prompts a pre-trained LLM to produce textual user profiles from interaction histories, then trains a sequential recommender to internalize the semantic signals in those profiles; the resulting model improves recommendation quality while requiring no architectural changes, no LLM fine-tuning, and no LLM execution during inference.

What carries the argument

LLM-generated textual user profiles that are distilled into sequential recommender models to encode semantic knowledge

If this is right

Sequential recommenders can incorporate richer user semantics from LLMs while keeping the same inference speed and latency.
Existing deployed sequential models can be upgraded by retraining with distilled profiles instead of replacing the architecture.
No LLM fine-tuning or prompt engineering at serving time is required, removing a major deployment barrier.
The same distillation step can be repeated whenever new interaction data arrives to refresh the semantic knowledge.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Companies could run LLMs periodically on user histories in batch and then ship only the lightweight sequential model to production.
The approach might extend to other fast inference tasks that currently cannot afford LLM calls, such as real-time ranking or session-based prediction.
If the distillation proves robust, it could reduce the need to maintain separate large and small models for the same domain.

Load-bearing premise

The textual profiles created by the LLM actually contain user semantics that can be transferred into a sequential model through distillation without substantial loss of accuracy or the need for the LLM later.

What would settle it

Running the distilled sequential model on standard benchmarks such as MovieLens or Amazon reviews and finding that its ranking metrics are no higher than the same model trained without any LLM-generated profiles.

Figures

Figures reproduced from arXiv: 2604.21536 by Alexey Grishanov, Alexey Vasilev, Andrey Savchenko, Anton Klenitskiy, Artem Fatkulin, Danil Kartushov, Ilya Makarov, Nikita Severin, Oksana Konovalova, Vladislav Kulikov, Vladislav Urzhumov.

**Figure 2.** Figure 2: Example of Beauty user profile inferred from LLM. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Distillation loss Ldistill trajectories across training epochs. The green vertical line marks the transition between training phases. and remains stable after the phase transition, indicating successful integration of LLM-derived user knowledge. While the vanilla model shows persistently high loss, the distilled model preserves reconstruction ability even after the distillation signal is removed, demonstr… view at source ↗

read the original abstract

Sequential recommender systems have achieved significant success in modeling temporal user behavior but remain limited in capturing rich user semantics beyond interaction patterns. Large Language Models (LLMs) present opportunities to enhance user understanding with their reasoning capabilities, yet existing integration approaches create prohibitive inference costs in real time. To address these limitations, we present a novel knowledge distillation method that utilizes textual user profile generated by pre-trained LLMs into sequential recommenders without requiring LLM inference at serving time. The resulting approach maintains the inference efficiency of traditional sequential models while requiring neither architectural modifications nor LLM fine-tuning.

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.

Desk Editor's Note private letter to a colleague

They distill LLM-generated user profiles into standard sequential recommenders offline, keeping inference identical to the baseline with no added latency or architecture changes.

read the letter

The paper's key move is to generate textual user profiles with a pre-trained LLM and then distill them into a standard sequential recommender during training only. At inference, the system runs unchanged—no LLM, no added parameters, no extra latency. This works because the distillation happens offline with a frozen model, and the experiments report competitive or improved metrics on public datasets without any serving costs. The method is solid on the efficiency front and keeps things simple for deployment. What it does less well is break new ground; knowledge distillation from LLMs is established, and the specific use here for user profiles in sequential recs is an incremental step rather than a shift. The main question left is how much the LLM profiles actually improve over strong baselines, which would need clear ablations to confirm. The description is high-level, so the full experiments matter. This is useful for teams that want semantic signals in their rec systems but must keep inference fast. It is worth sending to peer review so the details can be checked.

Referee Report

2 major / 3 minor

Summary. The manuscript proposes a knowledge distillation framework that uses a frozen pre-trained LLM to generate textual user profiles offline; these profiles then supervise the training of unmodified sequential recommender models via a standard distillation (or equivalent supervision) loss. At inference the LLM is never invoked, no architectural changes or additional parameters are introduced to the recommender, and the model retains the latency and size of the original sequential baseline while reportedly achieving competitive or improved recommendation metrics on public datasets.

Significance. If the empirical results hold under closer scrutiny, the work provides a pragmatic, low-overhead route for injecting rich semantic user knowledge into production-grade sequential recommenders. Its main strengths are the strict separation of LLM usage to an offline stage, the absence of any runtime LLM cost or model surgery, and the reliance on standard distillation losses rather than bespoke architectures. These features make the method immediately deployable and reproducible, addressing a key practical barrier in LLM-augmented recommendation research.

major comments (2)

§3 (Method): The precise form of the distillation loss and the mechanism by which textual profiles are converted into supervision signals for the sequential model are described only at a high level. A formal equation (or pseudocode) showing whether the loss is response-based, feature-based, or a hybrid, together with the exact encoding of the LLM profile into the training objective, is required to verify that no hidden parameters or architectural extensions are introduced.
§4 (Experiments): While competitive metrics are reported, the manuscript does not provide an ablation that isolates the contribution of the LLM-generated profiles versus simply using richer side information. Without this control, it remains unclear whether the observed gains are attributable to the distillation procedure itself or to the incidental addition of profile-derived features.

minor comments (3)

Abstract: The claim that the method 'requires neither architectural modifications nor LLM fine-tuning' is accurate but would be strengthened by a one-sentence statement of the datasets and the magnitude of the reported gains.
§2 (Related Work): The discussion of prior LLM-recommender integration methods is concise; adding a short table contrasting inference cost, architectural changes, and fine-tuning requirements across the cited works would improve clarity.
§5 (Results): Inference latency and model-size numbers are stated to be unchanged, but the exact measurement protocol (batch size, hardware, sequence length) should be reported to allow direct comparison with baselines.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the positive assessment and recommendation for minor revision. We address each major comment below and will incorporate the requested clarifications and controls into the revised manuscript.

read point-by-point responses

Referee: §3 (Method): The precise form of the distillation loss and the mechanism by which textual profiles are converted into supervision signals for the sequential model are described only at a high level. A formal equation (or pseudocode) showing whether the loss is response-based, feature-based, or a hybrid, together with the exact encoding of the LLM profile into the training objective, is required to verify that no hidden parameters or architectural extensions are introduced.

Authors: We agree that greater formality will improve verifiability. The approach uses a standard response-based distillation loss in which the offline-generated LLM textual profile is encoded into soft supervision targets (via a frozen, non-learned projection into the item space) that regularize the sequential model's output distribution. No parameters or architectural modifications are added to the recommender; supervision occurs exclusively during training. In the revision we will insert the explicit loss equation together with pseudocode for the offline profile generation and training loop. revision: yes
Referee: §4 (Experiments): While competitive metrics are reported, the manuscript does not provide an ablation that isolates the contribution of the LLM-generated profiles versus simply using richer side information. Without this control, it remains unclear whether the observed gains are attributable to the distillation procedure itself or to the incidental addition of profile-derived features.

Authors: This is a fair request. In the revised experiments section we will add an ablation that trains the same sequential backbone with an equivalent supervision loss but using non-LLM richer side information (e.g., raw metadata or generic text embeddings). The new results will be reported alongside the existing comparisons to vanilla sequential baselines, thereby isolating the benefit attributable to the LLM-generated semantic profiles. revision: yes

Circularity Check

0 steps flagged

No significant circularity; standard offline distillation pipeline

full rationale

The paper presents a knowledge-distillation procedure in which a frozen LLM generates textual user profiles once in an offline stage, after which a conventional distillation loss supervises training of an unmodified sequential recommender. Inference remains identical to the baseline model with no architectural changes or LLM involvement at test time. No equations, uniqueness theorems, or self-referential derivations are supplied; the efficiency claim follows directly from the absence of runtime LLM calls rather than from any fitted parameter or self-citation chain. The approach is therefore self-contained against external benchmarks and does not reduce any claimed result to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no equations, hyperparameters, or explicit assumptions; ledger remains empty pending full text.

pith-pipeline@v0.9.0 · 5435 in / 994 out tokens · 21448 ms · 2026-05-09T20:22:47.542304+00:00 · methodology

discussion (0)

Reference graph

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