CaRL-EM: Cost-Aware Reinforcement Learning for Entity Matching with LLMs
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The Flow has not summarised this story yet — read it at arXiv Computation and Language.
Entity Matching using Decision Models explores a new class of large language model (LLM) approaches that output probabilities for predefined answer options instead of generating text. The study evaluates six decision models in a zero‑shot setting across eight benchmarks, comparing them to the generative LLM GPT‑6 Luna and the PLM‑based matcher Ditto. Results show that the top four decision models achieve mean F1 scores comparable to or slightly better than GPT‑6 Luna, outperform Ditto on most benchmarks, run faster and are cheaper, and can be further improved by a cascade that delegates uncertain pairs to an ensemble of generative LLMs.
The paper investigates three fusion paradigms—Merge, Mix RL, and multi‑teacher on‑policy distillation (MOPD)—for consolidating reinforcement learning with verifiable rewards (RLVR) across multiple domains. Experiments across model scales and a multi‑domain benchmark show that while overall performance differences are small, significant gaps can appear on specific tasks, and each method exhibits distinct training dynamics and constraints. Practical guidelines are offered: Merge for cheap fusion when experts exist, Mix RL for unified training with adjustable domain mixtures, and MOPD when preserving domain‑specific gains is paramount.
DE‑Venus is a unified, data‑efficient framework for reinforcement learning with verifiable rewards (RLVR) tailored to large language models. It structures the RLVR lifecycle into three modules—Active Data Selection, Weak Supervision Construction, and Training‑Time Supervision Refinement—allowing method‑specific decisions to be expressed as dataset transitions or online transformations while maintaining distributed execution contracts. Experiments on public benchmarks and three business scenarios show that DE‑Venus can preserve or improve model quality using only 10% of labels or 13% of relevant data, and can cut convergence steps by 63%–75% in selected business configurations.
arXiv:2510. 08233v3 Announce Type: replace Abstract: Diffusion large language models (dLLMs) are promising alternatives to autoregressive large language models (AR-LLMs), as they potentially allow higher inference throughput.
The paper introduces CE‑RAG4EM, a cost‑efficient framework that applies retrieval‑augmented generation (RAG) to entity matching (EM) by using blocking‑based batch retrieval and generation. It demonstrates that, in a zero‑shot setting, CE‑RAG4EM achieves comparable or better matching quality than naive RAG while significantly reducing end‑to‑end overhead. Experiments on nine real‑world EM benchmarks show that the approach closes the knowledge grounding gap for LLM‑based EM, especially on datasets with ambiguous or sparse attributes, and provides clear guidance on balancing accuracy and computational cost through blocking size and retrieval granularity.
The paper introduces POISE, a reinforcement learning algorithm that uses a model’s internal states as a value estimator to reduce variance in reinforcement learning with verifiable rewards (RLVR). By employing a lightweight probe that reads internal signals during the forward pass, POISE predicts baselines online and uses a cross‑rollout construction to keep gradients unbiased. Experiments on Qwen3‑4B and OLMo3‑7B‑Instruct‑DPO across six domains show POISE outperforms existing RLVR baselines, offering more stable training and a value model that generalizes across tasks and scales with the policy.