AgenticRec: End-to-End Tool-Integrated Policy Optimization for Ranking-Oriented Recommender Agents
AgenticRec attacks a key gap in LLM-based recommenders: existing agents rely on frozen reasoning chains and cannot learn from ranking feedback to refine tool use. The paper proposes a two-stage training framework that combines ReAct-style tool invocation with list-wise Group Relative Policy Optimization (GRPO) and Progressive Preference Refinement (PPR) for hard-negative mining. The work matters because it demonstrates that end-to-end reinforcement learning can align multi-step tool use with ranking objectives, moving beyond prompt-engineered agent workflows.
The paper presents a well-engineered training pipeline for tool-augmented recommendation agents, bridging reasoning, tool invocation, and ranking optimization. However, its theoretical contributions are incremental applications of existing RL techniques to recommendation, and critical practical concerns—including inference latency and scalability beyond small candidate sets—remain unaddressed. The empirical gains are real but evaluated on limited benchmarks.
The empirical gains in Table 1 are substantial across four Amazon benchmarks, with AgenticRec improving H@1 by up to 28% over prior state-of-the-art (LLaRA). The ablation study (Table 2) convincingly demonstrates that training-free tool use underperforms or degrades compared to pure reasoning, while end-to-end optimization yields consistent gains, validating that learned tool invocation outperforms static prompting. The tool usage statistics (Figure 3) show the policy learns to invoke tools strategically rather than exhaustively.
The theoretical propositions claim novelty for standard results. Proposition 4.1 proves GRPO is unbiased—a property inherited directly from the general GRPO derivation applied to ranking metrics. Proposition 4.2 frames bidirectional preference reasoning as minimizing a convex upper bound, which is essentially the standard logistic loss argument for pairwise ranking without new theoretical machinery.
The evaluation has significant limitations: candidate sets contain only 20 items, leaving generalization to large-scale retrieval (thousands of candidates) untested. The paper also omits critical efficiency analysis—while $T_{max}=10$ caps tool calls, the latency and API cost implications of iterative tool calling at inference time are not discussed.
The comparison to training-free agents is fair, but the adaptation of point-wise methods (TALLRec) to the ranking setting may disadvantage them. The claim that bidirectional preference reasoning tightens error bounds more effectively than positive-only supervision is supported only by the theoretical upper bound argument (Proposition 4.2) without empirical ablation against unidirectional alternatives. The paper does not demonstrate that PPR uniquely achieves gains that could not be obtained via standard pairwise DPO or contrastive learning.
The paper provides detailed prompts (Appendix A) and tool descriptions (Appendix B), but no code repository or data preprocessing scripts are released. Training requires 4 NVIDIA A800 GPUs with specific hyperparameters: group size 8, learning rate $1\times 10^{-6}$, and batch size 64, but random seeds, exact training time, and complete baseline adaptation code are omitted. The reliance on Qwen3-4B-Instruct without sensitivity analysis to backbone choice limits reproducibility. The hard-negative mining depends on the agent's own ranking violations, introducing training instability that is not quantified.
Recommender agents built on Large Language Models offer a promising paradigm for recommendation. However, existing recommender agents typically suffer from a disconnect between intermediate reasoning and final ranking feedback, and are unable to capture fine-grained preferences. To address this, we present AgenticRec, a ranking-oriented agentic recommendation framework that optimizes the entire decision-making trajectory (including intermediate reasoning, tool invocation, and final ranking list generation) under sparse implicit feedback. Our approach makes three key contributions. First, we design a suite of recommendation-specific tools integrated into a ReAct loop to support evidence-grounded reasoning. Second, we propose theoretically unbiased List-Wise Group Relative Policy Optimization (list-wise GRPO) to maximize ranking utility, ensuring accurate credit assignment for complex tool-use trajectories. Third, we introduce Progressive Preference Refinement (PPR) to resolve fine-grained preference ambiguities. By mining hard negatives from ranking violations and applying bidirectional preference alignment, PPR minimizes the convex upper bound of pairwise ranking errors. Experiments on benchmarks confirm that AgenticRec significantly outperforms baselines, validating the necessity of unifying reasoning, tool use, and ranking optimization.
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