HiLaR: Optimizing LLM Recommendation Reasoning via Hierarchical RL
_reachsumit · x · 2026-07-31
The paper "Hierarchical Latent Reasoning for LLM-based Recommendation" introduces HiLaR, a framework addressing the cost and effectiveness of Large Language Model (LLM) reasoning in recommender systems.
- Background: Explicit natural-language reasoning incurs substantial overhead, while existing latent reasoning methods insufficiently characterize layer-wise preference contributions.
- HiLaR Framework: Constructs temporal-guided hierarchical user preference representations, aligns them with multiple LLM latent reasoning states, and organizes the reasoning process from broad preferences to fine-grained current intents.
- Optimization: Optimizes the reasoning trajectory using final recommendation feedback combined with layer-aware process rewards (derived from the marginal target-likelihood gain of each state).
- Results: Experiments on four Amazon benchmark datasets show HiLaR generally outperforms strong sequential, generative, and LLM-based recommendation baselines.
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