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Source note3 min read

ScaleToT: Generalizing Structured LLM Reasoning for Billion-Scale Low-Activity User Modeling

Source

arXiv

arxiv.org/abs/2606.24605v1

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Source

Plain-Language Brief

The paper presents ScaleToT, a method that improves user modeling for low-activity users by learning structured reasoning from a small subset of Large Language Models (LLMs). It constructs typed user-state chains using an entropy-guided Tree-of-Thought (ToT) refinement procedure. The method makes the structured reasoning usable from sparse profiles by training a student model and transferring its reasoning representations to a lightweight profile encoder. ScaleToT was evaluated on lifetime value (LTV) prediction in a billion-scale advertising deployment, showing a 6.738% increase in LTV compared to full-population reasoning, while covering only 7.32% of the potential population.

What the Document Says

  • The paper introduces ScaleToT for improving user modeling for low-activity users using LLMs.
  • ScaleToT constructs typed user-state chains with an entropy-guided ToT refinement procedure.
  • The method uses teacher-curated chains to train a student model through SFT and OSIPO.
  • The student's reasoning representations are transferred to a lightweight profile encoder.
  • ScaleToT was evaluated on LTV prediction in a billion-scale advertising deployment.
  • An A/B test increased LTV by 6.738%, while offline reasoning covered only 7.32% of the potential population.

Key Concepts Mentioned

  • User modeling
  • Large Language Models (LLMs)
  • ScaleToT
  • Typed user-state chains
  • Tree-of-Thought (ToT)
  • Supervised fine-tuning (SFT)
  • Outcome-Driven Segment-Aware Implicit Reward Policy Optimization (OSIPO)
  • Lifetime value (LTV) prediction
  • Advertising deployment

Claims Made by the Document

  • ScaleToT improves user modeling for low-activity users.
  • ScaleToT can make structured reasoning usable from sparse profiles.

Evidence Used

  • The paper's introduction and methodology sections explain the approach taken by ScaleToT.

Methods / Approach

  • Entropy-guided ToT refinement procedure
  • Supervised fine-tuning (SFT)
  • Outcome-Driven Segment-Aware Implicit Reward Policy Optimization (OSIPO)

Limitations Stated by the Authors

  • The paper does not discuss the potential limitations of ScaleToT beyond the computational cost reduction.

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The effectiveness of ScaleToT may depend on the quality and coverage of the teacher-curated chains.
  • The generalizability of ScaleToT to other domains or applications is not clear.

Open Questions

  • How does the quality of the teacher-curated chains impact the performance of ScaleToT?
  • Can ScaleToT be applied successfully to other domains or applications beyond advertising?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The paper focuses on improving user modeling for low-activity users in an advertising context. The method may have potential applications in other areas, but this is not explicitly discussed.

Raw Abstract / Excerpt

Accurate user modeling often depends on rich interaction histories, which are unavailable for billions of low-activity users. Large Language Models (LLMs) can infer latent user states from static profiles, but this reasoning becomes unreliable when profiles are sparse, and applying an LLM to billions of users is prohibitively expensive. We present ScaleToT, which learns structured reasoning from a small LLM-processed subset and extends it to the broader low-activity user population. To improve reasoning reliability, ScaleToT constructs typed user-state chains with a bounded entropy-guided Tree-of-Thought (ToT) refinement procedure. To make this structured reasoning usable from sparse profiles, the teacher-curated chains are used to train a student model on static profiles through supervised fine-tuning (SFT) and Outcome-Driven Segment-Aware Implicit Reward Policy Optimization (OSIPO). ScaleToT then transfers the student's reasoning representations to a lightweight profile encoder, providing shared reasoning signals for the remaining users without LLM inference. We evaluate ScaleToT on lifetime value (LTV) prediction in a billion-scale advertising deployment. A randomized online A/B test increased LT30 by 6.738%, while offline reasoning covered only 7.32% of the potential population, greatly reducing compute cost compared with full-population reasoning.