SalesLoop: Reinforcement Learning from Performance Feedback for Sales Lead Ranking

• URL: http://arxiv.org/abs/2607.20655v1
• Date: Wed, 22 Jul 2026 18:26:57 GMT
• Title: SalesLoop: Reinforcement Learning from Performance Feedback for Sales Lead Ranking
• Abstract summary: SalesLoop is a performance-aware reward that encodes conversion outcomes weighted by ranking position and conversion velocity.<n>SalesLoop improves NDCG@K by +7.9% and P@K by +15.8% over the strongest static baseline.
• Score: 3.337528987886516
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lead ranking in Customer Relationship Management (CRM) systems faces a persistent challenge: models achieving high offline accuracy often underperform in production. We identify three fundamental gaps responsible for this disconnect: offline-online metric mismatch, pointwise-listwise objective misalignment, and temporal distribution drift. To address these gaps, we propose SalesLoop, a reinforcement learning framework that establishes a closed feedback loop between model predictions and real-world business outcomes. Our approach introduces (1) a performance-aware reward that encodes conversion outcomes weighted by ranking position and conversion velocity, and (2) Discriminative GRPO, a listwise optimization objective that adapts Group Relative Policy Optimization to discriminative ranking models. SalesLoop improves NDCG@K by +7.9\% and P@K by +15.8\% over the strongest static baseline. A 160-day production A/B test at a New Energy Vehicle manufacturer, spanning 16.5M leads and 280 sales specialists across two provincial markets, validates statistically significant cumulative lift of +4.7\% ($p=0.047$) and +8.7\% ($p=0.002$). In production, the ranking backbone achieves Top-10\% recall of 44.1\% and surfaces high-intent leads at $2.3\times$ the conversion rate of specialist baselines.

Related papers

• Learn What's Left, Not What's Mastered: Saturation Aware Advantage Reweighting for Multi-Reward Policy Optimization [60.89431018071735]
  Reinforcement learning with group-relative advantages has become the de facto standard for post-training language model reasoners.<n>We show that rollouts with distinct reward profiles can receive identical advantages, and all objectives are optimized with fixed relative weights regardless of their current level of saturation.<n>We introduce saturation-aware reweighting, which standardizes each reward objective independently and adaptively discounts its contribution according to a batch-level estimate of objective saturation.
  arXiv  Detail & Related papers  (2026-08-17T04:07:50Z)
• RAVEN: A Regime-Aware Variable-context Expert Network for Financial Time Series Forecasting [83.06074370551887]
  A fixed context window is mismatched to the time-varying optimal look-back of non-stationary price processes.<n>We propose a Mixture-of-Experts framework designed to adaptively determine the temporal context for each input sample.<n> Experiments on cumulative log-return prediction (HS300, S&P500) and fund sales forecasting demonstrate that RAVEN achieves SOTA performances, improves Pearson correlation by 9.2% on HS300 and 20.2% on S&P500, and reduces MSE by 18.2% on fund sales forecasting.
  arXiv  Detail & Related papers  (2026-06-23T02:11:04Z)
• Rethinking Sales Lead Scoring with LLM-based Hierarchical Preference Ranking [10.224056739324702]
  Sales lead conversion differs fundamentally from e-commerce recommendation due to prolonged decision cycles and multi-stage funnels.<n>Traditional lead scoring methods face severe challenges: sparse supervision, a semantic gap in unstructured CRM logs, and inability to capture relative lead priority.<n>We introduce an LLM-based discriminative framework for sales lead scoring, which supports joint modeling of structured CRM features and unstructured customer interactions.
  arXiv  Detail & Related papers  (2026-06-03T03:05:57Z)
• FinRAG-12B: A Production-Validated Recipe for Grounded Question Answering in Banking [0.6356558256426569]
  We present a unified data-efficient framework for training large language models (LLMs)<n>We optimize answer quality, citation grounding, calibrated and refusal under real-world deployment constraints.<n>The system is deployed at 40+ financial institutions, achieving a 7.1 percentage point improvement in query resolution.
  arXiv  Detail & Related papers  (2026-05-06T22:04:44Z)
• Sell More, Play Less: Benchmarking LLM Realistic Selling Skill [1.1560747347923173]
  SalesLLM benchmark is built from realistic applications covering Financial Services and Consumer Goods.<n>SalesLLM benchmark scores correlate strongly with expert human ratings.
  arXiv  Detail & Related papers  (2026-04-08T13:06:37Z)
• Unifying Group-Relative and Self-Distillation Policy Optimization via Sample Routing [79.88256756334327]
  Self-Distillation Policy Optimization (SDPO) addresses this by providing denser, more targeted logit-level supervision.<n>We propose Sample-Routed Policy Optimization (SRPO)<n>SRPO routes correct samples to GRPO's reward-aligned reinforcement and failed samples to SDPO's targeted logit-level correction.
  arXiv  Detail & Related papers  (2026-04-02T17:29:18Z)
• Let the Agent Steer: Closed-Loop Ranking Optimization via Influence Exchange [9.300614085821612]
  We present Sortify, the first fully autonomous LLM-driven ranking optimization agent deployed in a large-scale recommendation system.<n>The agent reframes ranking optimization as continuous influence exchange, closing the full loop from diagnosis to parameter deployment without human intervention.
  arXiv  Detail & Related papers  (2026-03-29T16:58:29Z)
• REAL: Regression-Aware Reinforcement Learning for LLM-as-a-Judge [83.2858110368572]
  We propose textbfREAL (underlineREgression-underlineAware Reinforcement underlineLThought), a principled RL framework designed to optimize regression rewards.<n>We show that REAL consistently outperforms both regression-aware SFT baselines and standard RL methods.
  arXiv  Detail & Related papers  (2026-03-17T21:19:08Z)
• RGAlign-Rec: Ranking-Guided Alignment for Latent Query Reasoning in Recommendation Systems [25.34524038198569]
  We propose RGAlign-Rec, a closed-loop alignment framework for proactive intent prediction.<n>We also introduce Ranking-Guided Alignment (RGA), a multi-stage training paradigm.<n>Our framework achieves a 0.12% gain in GAUC, leading to a significant 3.52% relative reduction in error rate, and a 0.56% improvement in Recall@3.
  arXiv  Detail & Related papers  (2026-02-13T14:38:02Z)
• MURPHY: Multi-Turn GRPO for Self Correcting Code Generation [55.66642560374686]
  Murphy is a multi-turn reflective optimization framework that extends GRPO by incorporating iterative self-correction during training.<n>We show that Murphy consistently improves performance, achieving up to a 8% relative gain in pass@1 over GRPO, on similar compute budgets.
  arXiv  Detail & Related papers  (2025-11-11T05:03:22Z)
• GRPO-CARE: Consistency-Aware Reinforcement Learning for Multimodal Reasoning [53.894789613838654]
  We introduce SEED-Bench-R1, a benchmark with complex real-world videos requiring balanced perception and reasoning.<n>Using SEED-Bench-R1, we find that standard GRPO, while improving answer accuracy, often reduces logical coherence between reasoning steps and answers, with only a 57.9% consistency rate.<n>We propose GRPO-CARE, a consistency-aware RL framework optimizing both answer correctness and reasoning coherence without explicit supervision.
  arXiv  Detail & Related papers  (2025-06-19T08:49:13Z)
• Improving Auto-Augment via Augmentation-Wise Weight Sharing [123.71986174280741]
  A key component of automatic augmentation search is the evaluation process for a particular augmentation policy. In this paper, we dive into the dynamics of augmented training of the model. We design a powerful and efficient proxy task based on the Augmentation-Wise Weight Sharing (AWS) to form a fast yet accurate evaluation process.
  arXiv  Detail & Related papers  (2020-09-30T15:23:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.