Token-Efficient Long-Term Interest Sketching and Internalized Reasoning for LLM-based Recommendation
Authors: Zhihao Ding
Jinming Li
Shuai Mu
Jieming Shi
Venue: ICLR 2026 Poster (2026)
Keywords: LLM-based Recommendation
Rating Prediction
Long-term Preference Modeling
Efficient Reasoning
Latent Reasoning
Primary area: other topics in machine learning (i.e., none of the above)
TL;DR: We propose an LLM4Rec framework that effectively handles long user histories while enabling efficient inference.

Abstract: Large language models (LLMs) can solve complex real-world tasks when prompted to generate chain-of-thought (CoT) reasoning, motivating their use for preference reasoning in recommender systems. However, applying LLM reasoning on recommendation faces  two practical challenges. First, LLMs struggle to reason over long, noisy user histories that often span hundreds of items while truncation discards signals needed to capture long-term interests. Second, in decoder-only architectures, CoT requires generating rationale tokens autoregressively, leading to prohibitive inference latency for real-world deployment. To address the challenges, we propose SIREN, a framework that enables effective LLM-based rating prediction via long-term interest sketching and internalized reasoning. First, instead of prompting raw histories, we build a compact, token-bounded interest sketch that preserves persistent preferences and suppresses noise. Specifically, we encode and cluster item descriptions to discover semantic topics, then compress each user’s history into a short list of liked and disliked topics, facilitating LLM reasoning. 
Second, we develop an internalized reasoning strategy for efficient inference. We adopt a two-stage training paradigm: (i) train the LLM to reason explicitly for rating prediction with rule-based reinforcement learning, since ground-truth CoTs are unavailable in recommendation; and (ii) learn to internalize CoT into model parameters through hidden alignment. At inference, the LLM directly generates the rating with near-CoT quality.
Extensive experiments show that SIREN reduces average input tokens by $48.7\%$ compared to raw-history prompting, outperforms existing methods while delivering over $100\times$ lower inference latency than CoT-based LLM recommenders. Code and data are available at https://github.com/TommyDzh/SIREN.

Published as a conference paper at ICLR 2026
TOKEN-EFFICIENT LONG-TERM INTEREST SKETCH- ING AND INTERNALIZED REASONING FOR LLM-BASED RECOMMENDATION
Zhihao Ding Jinming Li Shuai Mu Jieming Shi
The Hong Kong Polytechnic University BByteDance
huangyuzhaaggbbyteednnees com, dieming.shhi shi@pollllllllo yu. edu.hk
ABSTRACT
Large language models (LLMs) can solve complex real-world tasks when prompted to generate maain-of-though (CoT) reasoning. motivating their use for preference reasoning in recommender systems. However, applying LLM reasoning on recom- mendation faces two practical challenges. First, LLMs struggle to reason over long, noisy user histories that often span hundreds of items while truncation discards sig- nals needed to capture long-term interests. Second, in decoder-only architectures, CoT requires generating rationale tokens autoregressively. leading prohibitive inference latency for real-world deployment. To address the challenges, we pro- pose SIREN, framework that enables effective LLM-based rating prediction via long-term interest sketching and internalized reasoning. First, instead of prompting raw histories, we build a compact, bounded interest sketch that preserves persistent preferences and suppresses noise. Specifically, we encode and cluster item descriptions to discover semantic topies, then compress each user's history into short list of liked and disliked topics, facilitating L.LM reasoning. Second, we develop an internalized reasoning strategy for efficient inference. We adopt a two- stage training paradigm: (i) train the LLM reason explicitly for rating prediction with rule-based reinforcement learning, since ground-truth CoTs are unavailable in recommendation; and (ii) learn to internalize CoT into model parameters through hidden alignment. At inference, the LLM directly generates the rating with near- CoT quality. Extensive experiments show that SIREN reduces average input tokens by 48.7% compared to raw-history prompting, outperforms existing methods while delivering over 100x lower inference latency than CoT-based LLM recommenders. Code and data are available at https: //t::///iiii /gut com/ TommyDzh/ S SIREN
INTRODUCTION
Large language models (LLMs) have recently demonstrated strong problem-solving abilities, espe- cially when their reasoning capacity is activated by chain-of-thought (CoT) prompting (Wei et al., 2022; Achiam et al., 2023). Reinforcement learning further amplifies this ability, as seen in OpenAI- ol (Jaech al., 2024) and DeepSeek-RI R1 (Guo al., 2025), enabling LLMs to solve Olympiad-level mathematics (Castelvecchi, 2025) and real-world coding tasks (Jimenez et al., 2024). Motivated by these advances, recent work explores LLMS in recommendation tasks such as user rating predic- tion (Tsai et al., 2024a; Kim et al., 2025) and next item prediction (Bao et al., 2024; 2025). In rating prediction, for example, an LLM is prompted with a user's behavior history together with a candidate item's 's description, and is instructed to infer the user's preference and output the rating the user would give. Unlike traditional ID-centric recommenders (Cheng et al., 2016; Zou et al., 2023), LLM--bsed approaches exploit rich item semantics, alleviating cold-start, improving generalization, and offering interpretable recommendations (Wang et al., 2024b).
Despite this promise, two practical challenges limit deployment of LLMs in real-world recommenders. First, long and noisy histories undermine LLM reasoning for recommendation. practice, users can have hundreds of interactions within days, producing histories that are lengthy, redundant, and
"Coreesooniigg author

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Published as a conference paper at ICLR 2026
Given the user's 's histary:
(User past interactions here)
Answer-only (AO)
CoT
What rating the give to /Candidate item]?
1.30
Avg: Latency (sisample)
Large I Language Model
20
7,219
7,234
7265
7290
5,59
MAE
1.08
Prompt
wens
16'
(c) Answer-only (AO)
0.82
10022
0.045
0,08t
The
user, prefers
0.50
Large Language Model
10
20
30
40
50
10
20
30
40
50
Max History Length
Max History Length
Prompt
"The
user
Samswer
(a) Accuracy Comparison
(b) Lateney Comparison
(d) Chain-of-thought (Col)
Figure I: We vary max history length from 10 to 50 and compare answer-only vs CoT of Qwen3-4B
in rating prediction on the Movies category of the AmazonReviews dataset. Full setup in Sec. B.2.
(a) MAE (L) vs. history length. (b) Average per-sample latency (s). (c) AO: placing <answer> immediately after the query Prompt signals the model to generate only the final answer. (d) CoT:
the model first generates an multi-step reasoning token-by-token, then outputs the final prediction.
noisy (Wang et al., 2025). Naively feeding these raw histories to an LLM degrades performance as LLMs are short of long-context processing (Wang et al., 2024a; Du et al., 2025) and accumulated noise obscures the preference signal and impairs reasoning. As shown in Fig. ((a), when using LLM for rating prediction, the prediction error increases as the maximum history increases from to 50 items. Prior approaches truncate the most recent items (Lyu et al., 2024; Tsai et al., 2024a), sacrificing long-term signals (Chang et al., 2023), or prompt the LLM to summarize profiles (Kim et al., 2025; Wang al., 2025), which still requires processing long histories and adds computational cost. This calls for a token-efficient user representation that preserves robust long-term signals for LLM reasoning.
Second, latency from explicit reasoning hinders deployment of LLMs in recommendation. Currently, there are two strategies for LLM inference in rating prediction: answer-only (AO) and chain-of- thought (CoT). AO inference (Fig. I(c) takes the prompt and directly generates the rating, whereas CoT (Fig. I(d)) emits multi-step rationale before the final rating, and each additional token requires an extra forward pass. Although CoT improves accuracy, the longer output sequence inflates inference latency and serving cost (Lin et al., 2025), which conflicts with production requirements. As shown in Fig. 1(b). CoT incurs per-sample latency over 100x higher than answer-only. To accelerate inference, prior work either distills knowledge into smaller LLMs (Xu al., 2025) or employs efficient decoding strategies like speculative decoding (Lin et al., 2025) and latent-reasoning (Zhang et al., 2025). However, these approaches still require generating intermediate reasoning tokens, incurring extra time costs.
This work addresses both challenges with SIREN, a framework for leveraging LLM reasoning in rating prediction. SIREN introduces two key components: (i) Long-Term TTrm Interest Sketching. Instead of prompting with raw, lengthy histories, we build a compact, preserves user persistent preferences and suppresses noise. We encode item descriptions and cluster their embeddings to obtain fixed set of corpus-level semantic topics; each user's history is then aggregated over these topics into a small list of likes and dislikes. We combine this sketch with user recent histories to capture short-term interests, providing a token-efficient yet informative prompt for LLM reasoning. (ii) Internalized Reasoning. We aim for answer-only inference that produces the final rating without emitting rationale tokens while retaining the gains of COT. To this end, we adopt two- stage training paradigm: first, since ground-truth CoT rationales are unavailable recommendation, we train explicit CoT reasoning over the interest sketch via rule- based reinforcement learning: second, we learn to internalize CoT by aligning the answer token's 'ss hidden states under answer-only decoding to those induced by COT. Our hidden alignment transfers the effect of CoT into model parameters, enabling near-CoT quality with answer-one only latency. We conduct extensive experiments on two real-world recommendation datasets to evaluate SIREN. Results show that SIREN attains high token efficiency and strong rating- prediccion accuracy with low inference latency. On Movies, for example, SIREN reduces average input tokens by 78% compared to raw-history prompting, lowers MAE by
interest sketch that
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Published as a conference paper at ICLR 2026
20.1% over the runner-up baseline, and delivers over x00x lower inference latency than decoding. In summary, our contributions are as follows:
We identify two deployment challenges for LLM-based recommendation
noisy histories and latency from explicit reasoning- present SIREN, a unified framework that addresses both.
We propose Long-Term Interest Sketching, a token-efficient user representation that replaces raw, lengthy histories with a compact, corpus-level topic sketch capturing stable preferences.
We develop two-stage training paradigm: (i) learn explicit reasoning for rating prediction without CoT labels via RL,, and (ii) internalize this reasoning through hidden-state alignment, enabling answer-only decoding with near-CoT quality.
Through extensive experiments, SIREN achieves lower rating- srinngprediction error than traditional and LLM-based recommender baselines, while delivering over 100x lower inference latency than CoT-based decoding.
TASK FORMULATION
Let U/ be the set of users and I. the set of items. Each item i E I has an associated textual description (e.g., title/metadata) denoted d(i). For a user u E U, let the chronologically ordered interaction history be H., { (ú, d(i), Tui) i ]. where Tus is the observed rating. Given a candidate item i E I, the goal of rating prediction is to estimate the rating that user u will give to fus based on user history H. and the candidate item description d(i).
When using an LLM for rating prediction, the pair (Hu, d(t)) is first converted into a textual prompt by prompt function (.), and then fed to the L...-basee recommender model M to predict:
Fui = M (HH,, d(1)).
SIREN
Fig. 2 illustrates the overall framework of SIREN. SIREN comprises two main components: (i) Long- Term Interest Sketching (Sec. 3. compresses long, noisy histories into a compact, token-efficient sketch that preserves stable preferences. (ii) Internalized Reasoning (Sec. 3.2) uses two-stage training strategy to retain CoT gains while enabling answer-only decoding, accelerating inference.
3.1 LERG INTEREST SKETCHING
Given user's history Hi,, directly prompting an LLM with raw interactions is suboptimal histories can be long and noisy. On the contrary, truncating to recent items discards information critical for long-term preferences. In SIREN, we construct compact long-term interest sketch and prompt the LLM with this sketch instead of the raw history. The constructed sketch comprehensively captures user preference signals under strict token limit. As shown in Fig. 2, we first encode all item descriptions and cluster their embeddings to obtain shared set of corpus-level semantic topics. Given these topics, user's history is mapped to topic labels and aggregated of interests, partitioned into likes and dislikes according to the average rating, Finally, we form the prompt by concatenating this sketch with the user's recent interactions and the candidate item description, capturing both long-term and short-term preferences. The LLM takes this prompt to predict the rating. We detail each step below.
yield a small set
Item Topic Discovery. We represent each item by semantic topic rather than its raw description. This lets us aggregate many historical items that share a topic, so a user can be summarized by a small set of topics that highlight core interests and discard irrelevant detail. Because users have histories of highly variable length, discovering topics per user is unstable (especially for short histories) and produces topics that are not comparable across users. Instead, we discover corpus-level topies once over the entire item set I. Specially, we first embed item descriptions as:
c(i) End((1)) E RRd i i I,
where Enc(-) denotes the text encoder and e(i) is the embedding of item i. We then apply K-means over all embeddings to obtain K cluster (topic) centers:
[ KMeans (Means((e(i)) I)),
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(a) Long-term Interest Sketch
N Recent
Candidate Item
Sci-Fi Action
Prompt
User Histories
Text Encoding
Slow Horror
/Like]
K-means
Superhero
Sci-Fi Action
Item Set
Superhero
[Reasoning|
Item Topics
/Dislikel
The user prefers... [Rating]
Slow Horror
Long-tern Interest Sketch
(b) Internalized Reasoning
Hiddon Alianment Loss:
Format Reward
Rating Regression Reward
Catten
1 Σ
(1 cos (5g ( tn (ala))
<thinks
usery prefers
Kanswer>
<lanswer>
1-1
LBB0
Large Language Model
Large Language Model
AAO (g)
hoor
Prompt
Sahinks
user
<ithinks
Sanswer
5
Prompt
Kanswert
Stage Learning Explicit Reasoning
Stage 2: Internalizing Reasoning
Figure 2: Overview of SIREN. (a) Long-term interest sketching: user histories are encoded,
clustered into semantic topics, and aggregated into a compact sketch likes and dislikes, which is combined with recent interactions and the candidate item for LLM-based rating prediction. (b) Internalized reasoning: Stage trains explicit CoT reasoning with reinforcement learning (RL); Stage aligns hidden states of answer-only decoding with CoT decoding, enabling answer-only inference with near-CoT quality but much lower latency.
where μa is the k-th topic center. Each item is assigned topic label by finding the nearest center:
e(i) - pa 1|
arg
min ke K)
For cluster k, we collect the descriptions of the M items closest to its center μa and prompt the LLM to summarize them into concise textual topic name Tk-
User Interest Sketching. Given item topics E (1...., K). we aggregate a user's history Hi, into a compact long-term interest sketch. For the k-th topic, the user's interactions within that topic are:
(k)
(i, d(i), ru)) H., kkk
(5)
where (k)k) is the set of interactions assigned to topic k. We partition the topics the user has interacted with into likes and dislikes based on the average rating per topic:
Fu,k
Σ
Fui-
(6)
(k))))
(H.r)))
Then threshold the per-topic average and obtain:
T = { (T Pu,k 2 0),
T, = (Tk : O<FF. <0}.
(7)
where T and T. are the sets of topic names the user likes and dislikes, respectively. The user's long-term interest sketch is:
S, (T+ Tw)
This sketch replaces raw, lengthy histories with a small, interpretable topic-level summary that preserves persistent preferences under strict token budget and suppresses noise, providing stable user information for LLM reasoning.
Prompt Construction. To predict user u's rating for item i I. we build the prompt from the user's long-term interest sketch S,, the N most recent interactions HN() and the candidate item description d(i). Let function (o.) linearize these components into prompt (i)::
Tu (3) T Su, H((N) d(i))
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LLM predicts the rating as:
fui
(S H,))(())) d(()))
(10)
This construction captures long-term preferences through S, and short-term context through H (N)
under strict token budget, providing concise but informative imput for rating prediction.
3.2 INTERNALIZED REASONING
While explicit CoT improves rating prediction (Fig. 1), it requires generating many rationale tokens, which inflates inference latency and serving cost. Our goal is answer-only inference that generates only the final rating without losing the quality gains of CoT. As shown in Fig. 2, SIREN adopts a two-stage training paradigm: (i) learn explicit CoT reasoning over the long-term interest sketch prompts for rating prediction; (ii) hidden alignment that trains the CoT model to internalize the explicit reasoning to model parameters. In this way, SIREN enables LLM decoding at inference with near CoT quality. The details are below.
Learning Explicit Reasoning via Reinforcement Learning. In the first stage, we train the model to explicitly reason over user sketch prompt (t) for rating prediction by generating rationale followed by a rating fui. Since ground-truth CoT's are unavailable in recommendation, we adopt reinforcement learning (RL) to optimize reasoning quality guided by rule-based rewards. We fine-tune LLM with Group Relative Policy Optimization (GRPO) (Shao et al., 2024), lightweight, critic-free RL update for LLMs.
To adapt GRPO in rating prediction, we combine format reward 8format that enforces the model to generate its reasoning within <thinksj..|</thhn
followed by the final prediction within Kanswer> /ensser>)..k//anseerr> answer>, and rating regression reward State that maps prediction error into rewards ranges from 2- 2j. Let rui E [a, bj denote the ground-trute rating user u has on item i, Aui b] the prediction (clipped to [a, bl), and Emax = b the maximum possible error. The per-sample reward is defined as:
+1,
format correct, (11) otherwise.
R(fai Tai)
Sformat
+
2
4 Emaxe
Pivi Tuil)
Sformat
1-.
9raze
Here Syate E 2, decreases linearly with the absolute prediction error, and Stormat E f(-1,+1) enforces the output format. GRPO then optimizes the model using R(frs,,Tui encouraging well- formed rationales and ratings close to the ground truth. GRPO algorithm is deferred Appendix
Learning to Internalize CoT via Hidden Alignment. In the second stage, we train the LLM to internalize its reasoning. Previously, the model has learned to reason explicitly over long-term interest sketches. The objective of reasoning internalization is to produce the final rating without generating intermediate CoT tokens, which reduces inference latency significantly. To this end, we adopt a hidden-alignment training strategy. We observe that, for a trained LLM MM from previous stage, the final rating prediction under both answer-only (Fig. I()) and CoT (Fig. (dd) decoding depends on the hidden state the Kanswer>| token. Consequently, if we align the answer-only hidden state to its CoT counterpart, the resulting predictions coincide while no CoT tokens are needed.
As shown in Fig. 2, let (9) denote the hidden state of the <answer> query token q at layer of Ma when only the input prompt is provided, and let (g) denote the corresponding hidden state when both the prompt and CoT are present. We fine-tune Mg with the hidden alignment loss:
I
Lalign =
M I 1=1
1
(1 cos (AA
(x))))
(12)
where is the number hidden layers in Ma and sgl:] denotes the gradient operator. At inference time, without explicit CoT tokens, the model computes similar hidden states for Kanswer> and achieves rating-pred prediction quality comparable to explicit reasoning. In practice, we apply low-rank adaptation (LoRA) (Hu al., 2022) to the key and value projection matrices of each attention layer, following our theoretical motivation presented below.
Theoretical Justifications. We analyze why hidden alignment enables internalized reasoning below.
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Theorem 1 (KV Adaptation Equivalence). Let q denote the answer-token query, Q the set of question prompt tokens, and Z the set of CoT reasoning tokens. For the 1-th attention layer with key/value projections (W K W.),, define
h (g) F'(Q, q): [W Wbl),
h() F'(1Q, Z. [W Wbl).
where Fl returns the layer-l hidden state at q produced by a standard attention block followed by feed-forward network (FFN) block. Define the updated answer-only hidden
(lo == F(QQ, di [Wk + AWk. W + AW(1).
Then, under a first-order (linearized) treatment of the attention and FFN at layer I, there exist updates (AWk. AW;) such that
(g) = bc.r(g).
This provides the motivational basis for our V-targgeed low-rank adaptation strategy for hidden alignment. The full proof is deferred to Appendix A.
4 EXPERIMENTS
In this section, we conduct experiments to address the following research questions:
RQI: How does SIREN perform on rating prediction under compared with classic recommenders and recent LLM-based baselines?
RQ2: What is the inference efficiency of SIREN versus LLM-based baselines?
RQ3: Does interest sketch improve accuracy over raw history and summary-based methods?
RQ4: Does the internalize CoT via hidden alignment effectively bridge the gap between answer- only and CoT decoding?
4.1 EXPERIMENT SETUP
Datasets and Baselines. We evaluate on the Books and Movies categories from the Amazon Reviews 2023 dataset (Hou al., 2024)'. Dataset statistics are reported in Table 1. The Books split follows (Kim et al., 2025); the Movies split is curated by taking the first 150k in- teractions from the Movies category of Amazon Reviews 2023. Following prior work (Kim et al., 2025; Bismay et al., 2025), we apply the 5-core filter, iteratively removing users and items with fewer than five interactions, and perform stratified sampling keep positive (rat- ings 2 4) and negative (ratings < 3) examples relatively balanced (Bismay et al., 2025). We adopt a leave-last-out split (Hou al., 2024): for each user, the last two interactions are held out for validation and test, respectively, and the remainder are used for training. We compare SIREN with 9 competitors
cat-
Table 1: Dataset Statistics
egories. (i) Dataset Statistics (Kang et al., 2023), including User Avg. Rating and Item Avg. Rat- ing. (ii) Traditional Recommendation Methods, including Matrix Factorization (MF) (Rendle al., 2009) and P5 (Geng et al., 2022). (iii)
Dataset Train
Valid
Test
User
Item
Book Movie
94075
12222
11708
10440
9753
22097
2629
2629
2629
10874
LLM-based Recommendation, including LLM4Rate (Kang et al., 2023) (using DeepSeek-Rl R1 and Qwen3-4B backends), respectively. Rec-SAVOR (Tsai al., 2024b), and EXP3RT (Kim al., 2025). Details of these basclines and their implementations are provided in Appendix B.3.
Implementation Details and Evaluation Metrics. For SIREN and EXP3RT, we use Qwen3- 4B (Yang et al., 2025) as the backend LLM. We use BGE-M3 (Chen et al., 2024a) as the text encoder, set number of corpus-level semantic topics K 20, number of descriptions to generate topic name M = 100, threshold for separating likes/dislikes 0 = 4. For prompt construction, we include the most recent N user interactions, with N = 30 for the Books dataset and N = 10 for the Movies dataset. For explicit reasoning, summarizes the key hyperparameters for each stage of our pipeline. Additional implementation details are in Appendix B. For rating prediction, report MAE (Mean Absolute Error) and RMSE
train with GRPO using the VERL framework (Sheng et al., 2024). Table 15
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Table Overall performance for rating prediction in MAE (1) and RMSE (L). Rank indicates the average per-method rank achieved across all datasets and metrics. Bold: best. Underline: runner-up.
Books
Movies
Methods
Rank
MAE ())
RMSE (L)
MAE ())
RMSE (J)
Dataset Statistics
User Avg. Rating
0.4538 0,5071
0.8396 0.7567
0.9975
1.3419 2.0683
5,25
Candidate Item Avg. Rating
1.5205
Traditional Recommendation Methods
MF
0.5740
0.7259
1.1604
1.3060
5.25
P5
0,5591
0.7939
0.8655
1.3406
4.75
LLM- based Recommendation
LLM4Rate (DeepSeek-RI)
0,4509
0,8329
0,9222
1,4047
4.5
LLM4Rate (Qwen3-4B)
0,4730
0.8916
0.7995
1,4797
5.75
Rec-S SAVOR
0,4893
0,8902
1.0884
1.5183
7.25
Exp3RT SIREN
0,4001
0.7567
0.9521
1.4646
0.3510
0.6887
0.7603
1.2924
(Root Mean Squared Error) following (Chen et al., 2024b; Kim et al., 2025). Lower values indicate better performance for both metrics. Following EXP3RT, we conduct each experiment with three random seeds and report the mean.
4.2 OVERALL PERFORMANCE
Rating Prediction Accuracy (RQ1). Table 2 reports rating prediction results for SIREN and baselines. We summarize three observations. (1) SIREN achieves the best MAE and RMSE on both datasets, often by large margin. For example, on Books, SIREN improves over the strongest LLM baseline, EXP3RT, by 12.45% in MAE and 8.99% in RMSE, indicating more accurate predictions with fewer large errors. (2) -Lsd methods generally outperform heuristic/statistical and traditional recommendation baselines in MAE, reflecting the benefit of leveraging item semantics. However, observe weaker gains in RMSE, sometimes trailing MF. A closer look suggests label- imbalance bias (datasets skewed toward positive ratings) leads LLMS to over-predict high ratings (c.g.. 2 4), which increases large-error penalties. By contrast, SIREN mitigates this via RL-based CoT training (GRPO) and subsequent internalization, improving robustness on minority low-rating cases. (3) Among LLM baselines, no single prior method dominates. Notably, EXP3RT, fine-tuned on CoT labels, does not consistently surpass LLM4Rate with zero-shot prompting, highlighting the importance both user representation and training strategy. contrast, SIREN consistent improvements stem from long-term interest sketching, which preserves comprehensive, noise-resilient preference signals under a strict token budget, and internalized reasoning, which retains CoT-quulity prediction while decoding answer-only.
Inference Efficiency (RQ2). We compare average generated output tokens and per-sample inference latency (s) for SIREN with answer-only decoding (SIREN-AO), its explicit CoT variant from Stage I Sec. 3.2 (SIREN-CoT), and LLM-based com- petitors (EXP3RT, LLM4Rate). We
SIREN-AO SIREN-CUT
EXP3RT
LLM4Rate
vLLM (Kwon et al., 2023) on single H20 GPU with batch size 1. We maximum out- put size as 1024 tokens and report per-sample inference latency (seconds) and the average number of generated output tokens, averaged over the full test of each dataset. As shown in Fig. 3: (i) explicit reasoning emits hundreds of rationale tokens, whereas SIREN-AO gen- erates single answer token; (ii) accordingly, SIREN-AO incurs substantially lower latency (about 0.013 s/sample both datasets), yield- ing over 100x speedup. Together with the accuracy results in Table 2, these findings indicate that SIREN achieves state-of-the-ane effectiveness and better efficiency for rating prediction.
Arg. Octput Toleets
600
2.15
Avg 1 Latency
800
238
234
200
166
162
10
Books (a) Token Genenation
Movies
Movies (b) Latency (log scale)
Books
Figure 3:
Comparison of LLM-based methods in average latency (s/sample) versus average number of generated output tokens (per sample).
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4.3 IN-DEPTH ANALYSIS
Study on Long-term Interests Sketching (RQ3). To examine the effectiveness of our proposed long- term interest sketch (Sec. 3.1), we compare different user modeling strategies for rating prediction. We fine-tune the LLM with answer-only labels, and evaluate prompts constructed from: (i) the most recent N interactions (Recent History), (ii) recent history augmented with our proposed sketch (+Sketch), (iii) recent history extended with additional past interactions until reaching the token budget (+Mo History), and (iv) user profiles summarized by LLMs as in Kim et al. (2025) (+ (roffil)). Table shows that our sketch achieves consis- tent improvements over recent history, attaining the best performance except for one MAE case on Books where it ranks second. Adding more histories sometimes reduces MAE but degrades RMSE, suggesting that longer raw histories in- troduce noise that amplifies large errors. In con- trast, LLM-generated profiles fail to improve upon recent history, highlighting the difficulty of off-the- summarization in capturing key user preferences for accurate rating prediction.
Table 3: Comparison of performance under differ- ent user modeling designs.
Books
Movies
Strategy
MAE
RMSE
MAE
RMSE
Recent History
0.3547
0.7131
0.7775
1.3635
+Sketch (ours)
0.3536
0.7114
0.7695
1.3556
+More history
0.3535
0.7153
0.7695
1.3723
+Profile
0.3563
0.7244
0.7779
1.3872
Study on Internalized Reasoning (RQ4). We evaluate strategies for converting GRPO-trained, explicit CoT model into answer-only (AO) inference, with all variants initialized from the same Stage-1 (GRPO-CoT) checkpoint. We post-train GRPO-traiie -tainne model with: (i) CE: supervised fine-tuning rating labels using cross-entropy loss; (ii) KD; logits-based knowledge distillation from the GRPO-CoT teacher (Hinton, 2014); (iii) KD+CE: joint distillation and cross-entropy:
(iv) HA: our hidden alignment loss; (v) HA+CE: hidden alignment combined with cross-entropy. Fig. 4 summarizes results, with the GRPO-CoT reference shown as a red dashed line, Across Books and Movies, HA consistently yields the lowest error and closely tracks the GRPO-CoT teacher, indicating that aligning the hidden states effec- tively internalizes CoT into the model param- eters while preserving accuracy under answer- only decoding. Interestingly, HA even surpasses the GRPO-CoT teacher in Books, likely because aligning hidden states transfers the rationale structure while avoiding the noise and variance of explicitly generating CoT tokens. contrast, HA+CE underperforms HA, suggesting token- level CE pulls the model toward label-only fit- ting which conflicts with latent structure induced by COT. KD-based variants remain behind HA, showing the benefit of aligning latent states over only matching output distributions.
0.354
Books
Movies
0,353
0.352
CoT 03351
0.00
0.755
GANO-CUT 7577
07832
TINT
0.351
0 2200
10
EDxCH
BA-C6
HA (Dom
KD
KDvCE
HA-CE
H4 (hoon
Figure 4: Comparison of strategies for internal- izing CoT reasoning, All variants are initialized from the Stage-1 GRPO-CoT model; the GRPO- CoT teacher is shown red dotted line.
Effect of Different LoRA Target Modules. To internalize CoT into the model parameters, apply LoRA adapters and train with the hidden- aaldennalignment loss in Eq. 12. Table 4 compares SIREN when adapting different target module sets: (i) All-linear (all linear projections), (ii) QKV (all attention projections), (iii) QV (query and value), and (iv) FFN (feed-forward layers only). Overall, adapting KV yields the best results on almost all datasets and metrics, while increasing the adapta- tion scope to all-linear does not consistently improve accuracy under the same training recipe. These find- ings align with Theorem 1, which motivates KV as a sufficient locus to absorb CoT effects, even though broader adaptation
Table 4: Different LoRA target modules.
Books
Movies
Modules
MAR
RMSE
MAE
RMSE
KV
0.3503
0.6949
0,7581
1.2947
all-linear
0.3505
0.6992
0.7577
1.2987
QKV
0.3505
0.6977
0.7581
1.2985
QV
0.3508
0.6971
0.7623
1.2851
FFN
0.3504
0.6979
0.7604
1.3073
sometimes be competitive.
Ablation Study, We conduct an ablation study that progressively incorporates our proposed designs.
In Table 5, we begin with answer-only fine-tune LLM on the most recent history (AO-SFT (Recent)). Adding our proposed long- term interest sketch in Sec. 3.1(AO-SFT) (+Sketch)) consistently reduces MSE while keeping latency nearly unchanged, confirming the benefit of token-efficient user
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Table 5: Ablation Study,
Table 6: Full item descriptions vs. brief text (title+category).
Books
Movies
Method
MAR(()
RMSF (1)
Lateney (t) MAR (1)
RMSF ())
Lateney (1)
Method
Books
Movics
AO-SFT (Recent) (Recen)))
0.3547
0.7131
0.811
0.7775
1.3635
0.012
RMSE
MAE
RMSE
AO-SFT (+Sketc))
0.3536
0.7114
0.013
0.7695
1.3556
CVT-GIRPO (-Sketeh)
0.3521
0.6977
2.22
0.7577
1.2821
2.15
Sketch (detail)
0.3836
0.7114
0.7695
1.3556
AO-HA (from C)
0.3503
0.6887
0.013
0.7603
1.2924
0.013
Sketch (bricf)
0.3594
0.7159
0.7706
1,3550
representation. Replacing answer-only supervision with GRPO training (Sec. 3.2) that enables explicit COT reasoning (GRPO-CoT (+ +Sketch)) improves MAE but incurs more than 200 higher latency due to autoregressive rationale generation. Finally, applying our hidden alignment (AO-HA (+Sketch)) internalizes the reasoning ability into model parameters, achieving the best MAE with answer-only latency. These highlight that long-term interest sketching improves representation, GRPO strengthens reasoning, and hidden alignment successfully bridges CoT accuracy with AO efficiency.
Comparison of Full Item Descriptions vs. Brief Text. We assess the robustness of long-term interest sketching when item text limited. We construct brief variant using only titles and categories (excluding detailed descriptions) for item embeddings and topic clustering, and fine-tune with answer-only supervision. Compared the main setting (full item descriptions), Table 6 shows that brief text yields a small but consistent drop in performance, confirming the value of richer textual signals for topic discovery and sketch quality. However, the degradation is minor, indicating that pretrained LLMs can extract useful semantics even from short texts. Since full descriptions are available in our datasets (Hou et al., 2024) and typically accessible in practice, we recommend using richer item text for encoding and clustering when possible.
Ablation on Rating Reward. To assess the impact of the rating reward, we ablate the lin- car, distance-awarr reward Srate (Eq. 11) with a binary exact-match reward, keeping the format reward and all other GRPO settings unchanged. Specifically, the binary variant assigns +2 for an exact rating match and -2 otherwise; the format reward and GRPO recipe are unchanged. Table 7 shows that the distance-aware Srate consistently outperforms the binary reward across datasets and metrics. This improvement is due to (i) denser learning signals- near-correct predictions receive graded credit rather than all-on no-thing feedback and (ii) smoother updates, as the reward varies continuously with error, yielding more stable GRPO training.
Table 7: Ablation on the rating reward.
Method
Books
Movies
MAE
RMSE
MAE
RMSE
Binary exact-mutch
0,3555
0.6997
0,8094
1.4201
Regression (Seate)
0.3520
0.6962
0.7577
1.2822
Different Backbones. To assess SIREN across LLM backbones and scales, we substitute Qwen3-4B with L.ama-3.2-38- Instruci under identical training settings. We report both the explicit CoT vari- ant trained with GRPO (SIREN-CoT) and the internalized reasoning version (SIREN-AO, token), alongside Exp3RT on each backbone. As shown in Table 8, SIREN-COT on Llama-3.2-3B slightly outperforms Qwen3-4B, while SIREN-AO achieves similar results and maintains the 1-token latency. On both backbones, SIREN consistently surpasses Exp3RT, demonstrating effectiveness and robustness across architectures and scales.
Table 8: Different Backbones.
Backbone
Method
Books
Movies
MAE J
RMSE 1
MAE J
RMSE
Exp3RT
0.4001
0,7567
0.9521
1,4646
Qwen3-4B
SIREN-CoT
0.3521
0.6962
0.7577
1.2822
SIREN-AO
0.3503
0.6887
0.7603
1,2924
ExpART
0.3616
0.7048
0.9209
1.4157
L.2.3
SIREN CoT
0.3481
0.6956
0.7524
1.2682
SIREN-AO
0.3534
0.7042
0.7622
1,2902
Candidate Reranking on IMDB. To as-
Table 9: IMDB candidate reranking. Metrics are Recal- 1/DDCG (t); latency per query in seconds (J).
sess generalizability beyond rating predic- tion, we follow the EXP3RT protocol and evaluate SIREN on candidate reranking using the IMDB dataset (Kim et al., 2024). LightGCN (He et al., 2020) retrieves the t0p-20 candidates per user; LLM-based models rerank these by predicted relevance under matched token budgets and identical candidate lists. We compare SIREN with EXP3RT. As shown in Table 9, SIREN consistently outperforms EXP3RT across all metrics, e.g., SIREN improves Recall @5 @5 by 14.7% and achieves 52x lower latency, demonstrating higher efficacy and efficiency. Combined with our rating-prediction results, these findings support SIREN 's effectiveness and generalizability.
Method
R@3
R@5
nDCG@3
nDCG@5
Latency (s)
EXP3RT
0.0161
0.0578
0.0149
0.0413
1.56
SIREN
0.0256
0.0663
0.0256
0.0426
0.03

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More Experiments. In Appendix B.4, we evaluate the influence of number of topics K in Fig. 5, effect of different hidden alignment distance function in Table 10, and qualitative analysis on GRPO- trained reasoning over the interest sketch in Table 14.
RELATED WORK
LLMs-based Recommendation. Inspired by the success of LLMs across multiple domains (Feng et al., 2023; Shao et al., 2024; Feng et al., 2024), researchers increasingly explore L.Ms as end-to-end recommenders for rating prediction (Tsai et al., 2024a; Kim et al., 2025), sequential recommenda- tion (Chen et al., 2024b; Zheng et al., 2024), and next-item prediction (Bao et al., 2024; 2025). Early work evaluates LLMs for rating prediction via prompt engineering (Achiam et al., 2023; Grattafiori et al., 2024), reporting promising performance, especially after fine-tuning (Kang et al., 2023), along with improved explainability and generalization (Liu et al., 2023). To further enhance preference modeling, recent studies leverage LLM reasoning for recommendation (T'ai et al., 2024b; Kim et al., 2025; Bismay et al., 2025). Rec-SAVOR (T'ai et al., 2024b) and ReasoningRee (Bismay et al., 2025) employ a stronger teacher I L.M to generate rationales for user preferences, which are then used to fine-tune a smaller student model, improving recommendation accuracy and providing human-interpretable explanations. To address long-term user preferences beyond truncated histories, several works incorporate long-range signals into LLM-based recommenders. EXP3RT (Kim et al., 2025) employs teacher LLM to generate user/item profiles and step-by-step reasoning, which supervise student fine-tuning. LLM-TUP (Sabouri et al., 2025) uses LLMs to produce short-term and long-term preference descriptions, which encoded and fused via attention for richer user embeddings. HyMiRec (Zhou et al., 2025) reconstructs full historical sequences using residual codebook quantization, then aggregates them with a lightweight recommender into coarse interest embeddings. Unlike prior work, SIREN targets two key challenges in LLM-based rating prediction. First, instead of truncating histories (Tsai al., 2024b; Bismay et al., 2025) or relying on free-form summaries (Kim et al., 2025; Sabouri et al., 2025), we introduce a token-budgeted interest sketch that discovers dataset-level topics once and represents each user with a small, human-readable like/dislike list, stable under strict token budgets. Second, to mitigate the latency of explicit reasoning, we train the model to internalize CoT, enabling answer-only inference that achieves near-CoT quality with substantially lower latency.
Inference Acceleration for LLM-based Recommendation. Despite strong accuracy, LLMs face deployment challenges in recommendation due to the latency of autoregressive decoding (Lin et al., 2025; Xu et al., 2025). A common direction is knowledge distillation (KD), transferring knowledge from large teacher et al., 2024c; Xu et al., 2025). More recently, efficient reasoning strategies have been explored in recommendation. For example, AtSpeed (Lin et al., 2025) incorporates speculative decoding to accelerate generative recommendation by drafting multiple top- K item sequences with a lightweight model and verifying them through target LLM, optimizing both top-K alignment and verification efficiency. LatentR3 (Zhang et al., 2025) introduces a latent-reasoning module that generates a small number of autoregressive latent tokens instead of long text tokens and trains them with a modified GRPO framework. However, these approaches still rely on producing intermediate tokens, which introduces extra decoding steps. contrast, SIREN eliminates rationale generation entirely by internalizing CoT into model parameters, enabling answer-only decoding while retaining the quality benefits of explicit reasoning.
smaller student to reduce parameters and speed up inference (Wang
6 CONCLUSION
We studied LLM- based rating prediction and addressed two practical challenges: long, noisy histories hinder an LLM's ability to reason about user preferences, and explicit CoT decoding incurs prohibitive inference latency. We introduced SIREN, a framework that addresses both issues. First, our proposed token-efficient long-term interest sketching compresses user's history into compact sketch that preserves persistent preferences while suppressing noise. Second, to enable efficient inference, we internalize reasoning via a two-stage procedure: the model trained to reason explicitly with reinforcement learning, then aligned to produce answer-only outputs by transferring CoT effects into the parameters through hidden aligmment. Extensive experiments show that SIREN improves accuracy under strict token budgets and achieves near-CoT quality at answer-only latency.
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ACKNOWLEDGMENTS
This work is supported by grants from the Research Grants Council of Hong Kong Special Ad- ministrative Region, China (No. PolyU 15205224), and Smart Cities Research Institute (SCRI)
70051036-P005644
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A PROOF OF THEOREM
Proof. Consistent with previous works (Li et al., 2024), we omit the softmax operation and the scaling factor approximate standard attention relaxed linear attention for qualitative analysis. We instantiate the layer-I map by the linear attention form
[[X]; [W Wi =
Ly
where X is the token matrix at the layer imput and Q22 is the layer-/ query for token 9.
CoT/AO difference reduces single additive term. Let XQ and Xz be the embeddings of the question tokens Q and the CoT tokens Z, respectively. Then
( (XQWt) (XQWk) d b (o)(()) = (IXq;XzlWe) (IXo:X2lWk) oy
(XQWW) (k) T0y + (X2W1) (X2Wk) Q²
Hence the residual is
(q) == (0) - h1) = (X2W3)
Exact equality via span containment. Consider answer-only with KV updates:
(4) = (XQ + AWL)) (Xo(Wk + AWk)) or
Expanding first order in LAW,,aWi ) the equality (g) h (1) requires
T XęaWi (k) Øg + XaW (XęAWk) Q₂ (X2W1) (X2 wk)
Define a == (XQWk) T and == x3 Q₁ the nondegenerate case a * 0 or b + 0, the set of attainable left-hand-side directions contains Col(XQ)- Consequently, the linear system is solvable iff
(2 T e Col(Xq).
The stronger span containments
Col(X²Wb) C Col(XqW() and Col(XzW Col(XoWk)
sufficient for solvability but not necessary.
General case (least-squares projection). When the span conditions fail, the least-squares problem
XoAWt (XQWk) To, + X&W, (x -
min AWR AWF
has solution that equals the orthogonal projection of (q) onto the subspace reachable by KV perturbations. This yields the claimed best (unconstrained) approximation.
B EXPERIMENT
B.1
IMPLEMENTATION DETAILS
Table 15 summarizes the key hyperparameters for each stage our pipeline. Additional specifics are provided below.
GRPO Training. For GRPO training in Sec. 3.2, we use the VERL framework (Sheng et al., 2024). The configuration includes max feox_pproppt_ wttppppoooepppppppaleelaeeeeleee 1ength:2046. max response length=512. con- stant learning rate x 10 6, KL loss coefficient 0.001, micro-batch size per GPU 32, rollout number 8, and training for 2 epochs.
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Hidden Alignment Training. For hidden alignment in Sec. 3.2, we build on TRL3. We fine-tune the model with parameter-efficient fine-tuning via LoRA, applied to the key and value projec- tions of each attention layer. LoRA hyperparameters are set to 8, lora alpha=16, and dora_dropout=0..e
Training uses DeepSpeed ZeRO Stage-2 for memory-efficien distributed optimization. We adopt AdamW W with learning rate 1 10-1, constant schedule with 100 warmup steps, and train for 3 epochs with batch size 64 on 8x H20 GPUs.
Latency Evaluation. For the latency study in Fig. 3, use vLLM (Kwon et al., 2023) on single H20 GPU with batch size 1. We set max_response__eneee per-sample inference latency (seconds) and the average number of generated output tokens, averaged over the full test set of each dataset.
as 1024 tokens and report
B.2 MOTIVATIONAL EXPERIMENT SETUP
For the introduction experiment, we evaluate Qwen3-4B without post-training on the Books split. We adopt the LLM4Rate prompt (Kang et al., 2023) to instruct the model to perform rating prediction from user history and candidate metadata, and measure performance while varying the maximum history length.
B.3 BASELINES
We provide more descriptions and implementation details of the baselines in Sec. 4.
MF (Rendle et al., 2009). A representative latent-factor collaborative filtering method for rat- ing prediction based solely user-item interactions. We use the Surprise Python library for implementation and tune standard hyperparameters on the validation set.
P5 (Geng et al., 2022). A foundation model for recommendation. P5 unifies diverse recommen- dation tasks by converting user-item interactions, item metadata, and user reviews into natural language sequences and training with language modeling objective. We adopt the official imple- mentation and fine-tune a T5 backbone for the rating-prediction task using the authors' prompt format.
LLM4Rate (Kang et al., 2023). Prompt-based rating prediction with off- off-the-shelf LLMs. The prompt includes the user's recent history (item titles, genres, ratings) and the candidate item's title and genres. In our setup, we evaluate DeepSeek-RI R1 and Qwen3-4B without fine-tuning instructing the model to generate a rationale followed by a rating. The number of recent interactions follows our main setting (N 30 for Books, N = 10 for Movies).
Rec-SAVOR (Tsai et al., 2024b). Uses an off-the-shelf LLM to produce a rationale and final rating from a compact window of recent interactions. Following the original design, we use at most 10 recent items and include rich item fields (title, description, categories, price) together with the corresponding review. We adopt the authors' prompt template and use DeepSeek-R the backbone LLM.. EXP3RT (Kim et al., 2025). Leverages user reviews to build profiles and distill reasoning. A teacher LLM first extracts preference signals from each review and summarizes user and item profiles; then generates step-by-step rationales and target ratings conditioned on these profiles. The resulting pairs supervise smaller student LLM. We use the official code release". Following their setup, we use DeepSeek-R1 to generate weffreeces/proflees/aaiionn 4B as the student, matching our backbone in SIREN. Because their profiles include average-rating statistics, we compute these statistics on the training split only to avoid leakage (the released averages differ from train-only values); we report results using the recomputed statistics.
and fine-tune Qwen3-
B.4 MORE EXPERIMENTAL RESULTS
Influence of the number of topics K. We study how the number of corpus-level topics (K) in the interest sketch affects performance. As shown in Fig. 5, we vary K E {10, 20, 30, 40, 50} on Books
hhttps://surrrissllib.co
'https: httpes//ggthhbb.com///ssg///////
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- MAB (
RMSE (L)
Books
Movies
1,400
0.720
1.375
0.710
Score (
1,350
0.360
0.790
0.780
0.355
0.770
10
20
30
10
20
30
Number of Topics K
Number of Topics K
Figure 5: Performance of SIREN when varying number of topies K.
Table 10: Performance of loss function used in Eq. 12.
Books
Movies
Method
MAE
RMSE
MAE
RMSE
answer-only SFT
0.3527
0.7214
0.7695
1.3556
GRPO
0.3521
0.6962
0.7577
1.2822
Cosine
0,3503
0.6930
0.7603
1.2924
MAE
0.3523
0.6937
0.7687
1.3001
MSE
0.3528
0.6940
0.7679
1.2939
and Movies and report MAE/RMSE. SIREN performs best with smaller K (c.g., 10-20). Because each dataset is already category- homogeneus (books movies), large K fragments items into overly fine-grained topics that capture trivial distinctions, weakening preference modeling and risking overfitting. Balancing accuracy and stability across datasets, set K! =120 for the main experiments to avoid excessive hyperparameter search while remaining within the empirically favorable range.
Effect of Different Hidden Alignment Distance Function. In the hidden alignment loss Eq. 12, default to cos evaluate alternative distance measures, including mean absolute error (MAE) and mean squared error (MSE). Results are reported in Table 10, with AO-SFT and GRPO-CoT included for reference. We observe that cosine consistently achieves the best performance, suggesting that preserving directional information is more important than matching absolute magnitudes when aligning hidden states.
cos similarity to measure the distance between hidden states of CoT and AO. Here, we
Training Cost Comparison. We compare SIREN against EXP3RT under the same hardware (2x) Because SIREN trains in two stages, we report Stage-1 GRPO and Stage-2 hidden alignment (HA) separately, as well as the total wall-clock time. As shown in Table 11, SIREN's cost is dominated by Stage-1 GRPO, while Stage-2 HA is lightweight. Despite the higher training time, SIREN delivers substantially lower inference latency (Fig. 3) and better accuracy (Table 2). To further reduce cost, plan explore coreset/importance data selection (Jiang et al., 2025) and dynamic sampling for RL (Yu et al., 2025).
C GRPO DETAILS
Group Relative Policy Optimization (GRPO) (Shao et al., 2024) is a reinforcement learning algorithm designed to activate LLM reasoning without relying on ground-truth chain-of-thought (CoT) labels. Unlike RL algorithms like PPO (Schulman et al., 2017), which estimates the advantage using a learned value function, GRPO computes advantages in group-relative manner. Specifically, for each input (q, a). the old policy Te., samples a group of candidate responses {or]< - Each response receives rule-based reward R2 (e.g., correctness of the final rating). The normalized group-relative
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Table 11: Training cost comparison on 8xH20 GPUs. We report time per epoch and total time costs ("overall").
Books
Movies
Method
time/epoch
overall
time/epoch
overall
Exp3RT
0.97h
2.91h
0.23h
0.70h
SIREN GRPO
9.18h
18.35h
3.80h
7.6lh
SIREN-HA
0.39h
1.18h
0.14h
0,42h
SIREN (total)
19.53h
8.03h
advantage for the i-th response is:
A R, mean (
(13)
std( (
The policy is then optimized using a clipped objective similar to PPO, with an additional KL. regularization term to control deviation from the reference policy mie
locl min (rs,,))AAs,, 1,, -3rr (To II Ter)
(0)
(14)
where (0) is the importance ratio between the current and old policies. GRPO thus leverages group-relative normalization to avoid reward hacking and removes the dependency on a value function.
In our setting, GRPO trains LLMs to produce explicit reasoning (CoT) for rating prediction, with combination of format reward and rating regression reward as overall reward. This provides foundation for our internalized reasoning stage, where we align hidden states to enable answer-only inference.
Qualitative analysis of GRPO-trained reasoning over the interest sketch. We examine how GRPO enables explicit CoT' reasoning over the sketch prompt. Table 14 shows Movies test case. The model (i) grounds its rationale in the likes/dislikes sketch (e.g., leveraging "military conflict & combatant perspectives") ), (ii) cross-checks the candidate against recent interactions (aligning Godzilla with the user's high rating on Rogue One), and (iii) produces a clean <think>... </think> rationale followed by a numeric prediction between <answer>... ... < /aasswrr>. This illustrates that GRPO- trained CoT can make good leverage of interest sketch to filter noise, highlight stable preferences, and yield interpretable alignment between evidence and prediction.
D PROMPTS
Table 12 shows the CoT prompt template used for rating prediction during GRPO training (Sec. 3.2). Table 13 presents the prompt for summarizing cluster (constructed from the M items nearest to (n) into a concise topic name.
E LLM USAGE
We used large language model solely for language polishing (grammar, phrasing, and copy-editing). It did not contribute research ideation, experiment design, implementation. data analysis, the creation of technical content (e.g., equations, algorithms, or results), All scientific claims, datasets, code, and citations were produced and verified by the authors, who take full responsibility for the content.
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Table 12: CoT prompt template used for rating prediction during GRPO training.
SYSTEM PROMPT
You intelligent recommender system assistant. Your task predict the rating (from 1.0 5.0) that user will give to candidate (categozy), y. based on the user's past behavior and preferences. You should first reason about how well the candidate item aligns with the user's historical preferences, and then output predicted rating. Your reasoning and the final rating must be wrapped with <think> </think> and <answer> < snnw tags, respectively. The final rating must numeric value between 1.0 and 5.0. not include any extra explanation after the <answer> tag.
ASSISTANT PROMPT TEMPLATE
The user's long-term interest sketch is: -long-term
sketch)
Below are the recent (category) ratings the format: Title, Genres, Rating. Ratings range from 1.0 to 5.0.
(N most recent interactions)
The candidate [category) is deseribed as: (candidate item description)
Based the above information, what rating will the user give?
Table 13: Prompt used to summarize cluster into a concise topic name.
SYSTEM PROMPT
You expert taxonomy editor. Given set of (M) [category] entries representing items nearest to the cluster center, produce single concise topic that best summarizes their shared theme.
[ISTRUCTIONS]]
Analyze the implicit commonalities across the provided (category) entries (title, tags, brief description). Focus on high-level semantics, not surface details.
[CONSTRAINTS]
Output single short phrase (S 10 words).
2. Use concise, abstract language (no full sentences).
3. Do not list specific titles or examples.
4. Output format:
Theme: <concise phrase>
ASSISTANT PROMPT TEMPLATE
Nearest-to-center items:
(M descriptions)
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Table 14: Qualitative example GRPO-trained reasoning the interest sketch.
Prompt
[SYSTEM]
You are an intelligent recommender system assistant. Your task predict the rating (from 1.0 5.0) that a user will give to candidate movie, based on the user's past behavior and preferences.
You should first reason about how well the candidate item aligns with the user's historical preferences, and then output predicted rating. Your reasoning and the final rating must be wrapped with <think> </think> and <answer> </ </answer> tags, respectively. The final rating be numeric in [1.0, 5.0). Do not include any extra explanation after the <answer> tag.
[USER: Sketch]
Likes (ratings 2 4J: Military Conflict & Combatant Perspectives
Dislikes (ratings S 3): Crime-solving with ethical ambiguity; Secrets & emotional turmoil in relationships
[USER: Recent ratings (Title, Genres, Rating)]
Superman: The Animated Series, Volume 3 (DC Comics Classic Collection),
Movies & TVIToday's Geals/Feature Deals New Releases | The Big DVD Blu-ray Blowout, 2
Romancing the Stone (Special Edition), Movies TV|Fully Loaded D Editions,
ROGUE ONE: STAR WARS STORY, Kovies Science Fiction & |
Fantasy Science Fiction| Star Wars, 5
[USER: Candidate item]
Godzilla: King of the Monsters Special Edition (DVD), Movies & EVi
Loaded DVDs Special Editions
Model Output
<think>
Okay, let's see. The user has specific preferences and dislikes. They like military confict and combatant perspectives, which means they probably enjoy action- packed, maybe war-related heroic stories. They dislike crime-solving with ethical issues and secrets in relationships. so maybe they don't like complex moral dilemmas emotional drama.
Looking their recent ratings: Superman: The Animated Series got 2, which bit low. That's a superhero movic, so maybe they like some action but not the animated style? Romancing the Stone is a 3, which is a romantic thriller. They might not like that much. Rogue One, a Star Wars movie, got a 5, so they like sci-fi, especially from Star Wars. The candidate movie is Godzilla: King of the Mon- sters Special Edition. That's sci-fi monster movie with action elements. Godzilla films often have a of action and are set in a post-war context, which might align with their preference for military conflict. The user liked Rogue One, which is also high-action sci-fi. The Special Edition DVD might be plus since they liked the Special Edition of Romancing the Stone. Considering their dislike for crime-solving and emo- tional issues, Godzilla's 's more about action and less about complex relationships, so should fit. The rating is likely 5. </think>
canswer>5.0</
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Table 15: Hyper-parameter (HP) settings of method each dataset. Shared values repeated
for completeness.
HP
Books
Movies
Backbone & Encoders
Backbone LLM Text encoder
Qwen3-4B BGE-M3
Qwen3-4B BGE-M3
Sketch Prompt Construction
Topics (K)
20
20
Topic naming (M)
100
100
Like/Dislike threshold (0)
4
4
Recent history length (N)
30
10
GRPO Training (Explicit Reasoning)
Framework
VERL
VERL
max max__rrompt__o prompp_lemgthh
2046
2046
max_ response_lengte
512
512
learning_nas rate
x 10
10
KL coefficient
0.001
0.001
micro batch size_per_ Bereeper_GPPUU GPU rollouts
32
32
8
8
epochs
2
Hidden Alignment
Target modules
K,
K,
LoRA rank (r)
8
8
lora ara_alpha
16
16
lora fora_dropout
0.05
0.05
optimizer
Adam W
Adam W
learning_rate
x 10
x 10
schedule
constant
constant
warmup_ster steppppsseppseeee
100
100
batch_ size (global)
64
64
epochs
3 DeepSpeed 7eRO-2 8x H20 GPUs
distributed setup
DeepSpeed ZeRO-2 8x H20 GPUs
hardware
Latency Evaluation (Inference)
engine
vLLM single H20
vLLM single H20
hardware
batch size
max response length
1024
1024
21


Reviews:
[{"review_id": "9u3fSpWf9U", "reviewer": "Reviewer_Zz5G", "summary": "The paper proposes a post-training method for LLM-based recommendation systems that can reduce input length by aggregating and clustering long histories. Through a two-stage training algorithm, it first provide the LLM with accurate recommendation capabilities, then uses hidden state supervision alignment to reduce the CoT part to zero, thereby achieving fast and accurate score computation. The work's motivation and experiments are relatively comprehensive, and the writing is fairly well-structured. However, the overall innovation is notably insufficient. In particular, the results of the ablation study (RQ4) actually demonstrate that the proposed method's optimizations for the input and output components show minimal improvement in effectiveness.", "questions": "In addition to the issues mentioned in the Weaknesses section, I would also like to ask the authors: \n1. The instability and randomness of RL are widely acknowledged in the community. Have the authors encountered similar issues, and were the experimental results averaged over multiple training runs as is common practice in the RL community? \n2. When selecting the reward function, was an ablation study conducted (especially for s_{rate}) to try different forms of reward functions?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper proposes a two-stage post-training method based on reinforcement learning that first elicits the model's reasoning capability through RL to achieve accurate score estimation, then shortens the CoT length through an aligned \"internalized reasoning\" stage, thereby accomplishing the objective of being \"both fast and accurate.\"\n\n2. The motivation is clear and highly valuable, addressing a problem that current LLM-based systems are actively working to solve. The paper provides comprehensive experimental results demonstrating the superiority of the proposed method over other related approaches, and is also well-written.", "weaknesses": "1. The paper suffers from a notable lack of innovation, applying mature techniques from the existing community to recommendation systems. However, neither Stage 1 nor Stage 2 represents a novel idea. Moreover, the ablation study results (Figure 4) indicate that these modifications show almost no difference from direct SFT (CE in the Figure 4), with only marginal improvements (considering the test set and randomness, my personal view is that these improvements are extremely minimal).\n\n2. As for the experimental results. Particularly in Figure 4, where simple CE achieves results very close to the original SIREN, I am unclear why your reproduced Exp3RT performs significantly worse (I noticed in the appendix that you used the same base model for training, but still have doubts about the results). Additionally, it should be noted that I did not find SFT results directly using ratings as labels in the paper, so I can only refer to your SFT results based on the Stage 1 model as the base (i.e., CE in Figure 4).\n\n3. There are also some minor writing issues, such as subscripts that should be in roman type but were overlooked (e.g., Eq. 12), \"Appendix ??\" on line 491, and non-standard citation formatting (e.g., the title of Kim 2025's paper is incorrect)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "0hmdjEZftQ", "reviewer": "Reviewer_5SEj", "summary": "This paper identifies two main limitations of LLM-based recommender systems: their difficulty in reasoning over long and noisy user histories, and their high inference latency. To address these challenges, the authors propose a method called SIREN, which employs long-term interest sketching to effectively process user histories and internalized reasoning to enhance inference efficiency. Experimental results demonstrate that the proposed method performs well.", "questions": "see weakness.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. The paper is well-motivated, and the two main limitations of LLM-based recommender systems it identifies are highly worth addressing.\n2. Experimental results show that the proposed method effectively mitigates these limitations, demonstrating solid empirical performance.\n3. The hidden state alignment component is particularly insightful.", "weaknesses": "1. In lines 254–256, the paper states that *“the final rating prediction under both answer-only (Fig. 1(c)) and CoT (Fig. 1(d)) decoding depends on the hidden state at the <answer> token.”* Could you provide additional evidence to support this claim?\n2. What does the “Rank” column mean in Table 2?\n3. Could you compare the training costs of these LLM-based recommender systems?\n4. I think Table 2 is missing an important baseline — one where the **first stage** extends the recent history with additional past interactions until reaching the token budget (*More history*), and the **second stage** uses **GRPO-CoT**.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "c10dz6FsnA", "reviewer": "Reviewer_NNTh", "summary": "The paper proposes SIREN, a framework to improve rating prediction with large language models (LLMs). It targets two deployment challenges: (1) long and noisy user histories that harm reasoning, and (2) high inference latency due to explicit CoT generation.", "questions": "See comments in Weaknesses.", "limitations": "", "rating": 4, "confidence": 2, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. Clear practical motivation: identifies real deployment issues of long user histories and expensive CoT reasoning.\n2. Elegant design: the “interest sketch + hidden-alignment” combination is conceptually simple yet effective.\n3. Significant efficiency gains: achieves near-CoT accuracy with minimal latency.", "weaknesses": "1. Limited dataset diversity. Experiments are conducted only on two domains (Books and Movies) from the Amazon Reviews dataset. The generalizability to ranking, Top-N recommendation, multimodal, or more complex scenarios remains unclear.\n2. Dependence on textual descriptions. The robustness of the encoding and clustering components should be further validated under conditions where item descriptions are scarce, noisy, or cross-lingual.\n3. Cross-model generalization. The evaluation is limited to Qwen3-4B. It would strengthen the paper to include experiments on models with different architectures and scales (e.g., Llama, Gemma).\n4. Insufficient discussion of related work. Several relevant studies are not discussed or cited, such as [1–2] on long-term user profiling and [3] on latent reasoning. \n\n\n[1] Temporal User Profiling with LLMs: Balancing Short‑Term and Long‑Term Preferences\n\n[2] HyMiRec: A Hybrid Multi‑interest Learning Framework for Long‑Term Multi‑interest Sequential Recommendation\n\n[3] Reinforced Latent Reasoning for LLM‑based Recommendation", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)