Reciprocal Co-Training (RCT): Coupling Gradient-Based and Non-Differentiable Models via Reinforcement Learning

• URL: http://arxiv.org/abs/2604.16378v1
• Date: Tue, 24 Mar 2026 20:21:31 GMT
• Title: Reciprocal Co-Training (RCT): Coupling Gradient-Based and Non-Differentiable Models via Reinforcement Learning
• Abstract summary: Large language models (LLMs) and classical machine learning methods offer complementary strengths for predictive modeling.<n>LLMs rely on gradient-based optimization over textual data, whereas models such as Random Forests (RF) employ non-differentiable feature partitioning.<n>This work introduces a reciprocal co-training framework that couples an LLM with an RF classifier via reinforcement learning.
• Score: 3.8126346464908067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) and classical machine learning methods offer complementary strengths for predictive modeling, yet their fundamentally different representations and training paradigms hinder effective integration: LLMs rely on gradient-based optimization over textual data, whereas models such as Random Forests (RF) employ non-differentiable feature partitioning. This work introduces a reciprocal co-training framework that couples an LLM with an RF classifier via reinforcement learning, creating an iterative feedback loop in which each model improves using signals from the other. Tabular data are reformulated into standardized textual representations for the LLM, whose embeddings augment the RF feature space, while calibrated RF probability estimates provide feedback signals that guide reinforcement learning updates of the LLM. Experiments across three medical datasets demonstrate consistent performance gains for both models, with particularly strong effects for the LLM. Ablation analyses show that iterative refinement, hybrid reward design, and dimensionality control jointly contribute to these gains. The proposed framework provides a general mechanism that allows incompatible model families to leverage each other's strengths through bidirectional adaptation.

Related papers

• REATS: LLM Reasoning-based Ensemble Learning for Adaptive Time Series Forecasting [33.69887280643071]
  We propose REATS, which processes temporal pattern descriptions and numerical features to produce interpretable ensemble weights.<n>Experiments on eight benchmarks demonstrate that REATS outperforms competitive ensemble baselines.
  arXiv  Detail & Related papers  (2026-08-10T19:04:46Z)
• LLMs as High-Dimensional Nonlinear Autoregressive Models with Attention: Training, Alignment and Inference [15.493230983626281]
  Large language models (LLMs) based on transformer architectures are typically described through collections of architectural components and training procedures.<n>We formulate LLMs as high-dimensional nonlinear autoregressive models with attention-based dependencies.
  arXiv  Detail & Related papers  (2026-01-31T00:37:53Z)
• An Integrated Fusion Framework for Ensemble Learning Leveraging Gradient Boosting and Fuzzy Rule-Based Models [59.13182819190547]
  Fuzzy rule-based models excel in interpretability and have seen widespread application across diverse fields.<n>They face challenges such as complex design specifications and scalability issues with large datasets.<n>This paper proposes an Integrated Fusion Framework that merges the strengths of both paradigms to enhance model performance and interpretability.
  arXiv  Detail & Related papers  (2025-11-11T10:28:23Z)
• FlexAC: Towards Flexible Control of Associative Reasoning in Multimodal Large Language Models [80.6268239673988]
  Multimodal large language models (MLLMs) face an inherent trade-off between faithfulness and creativity.<n>Existing methods lack the flexibility to modulate this reasoning strength.<n>We propose equipping MLLMs with mechanisms that enable flexible control over associative reasoning.
  arXiv  Detail & Related papers  (2025-10-13T09:22:12Z)
• Round-trip Reinforcement Learning: Self-Consistent Training for Better Chemical LLMs [51.29260537017623]
  Large Language Models (LLMs) are emerging as versatile foundation models for computational chemistry.<n>These models often lack round-trip consistency.<n>We introduce Round-Trip Reinforcement Learning (RTRL), a novel framework that trains a model to improve its consistency.
  arXiv  Detail & Related papers  (2025-10-01T23:58:58Z)
• Intrinsic Training Signals for Federated Learning Aggregation [13.540945877050525]
  Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy.<n>This work demonstrates that effective model merging can be achieved solely through existing training signals.
  arXiv  Detail & Related papers  (2025-07-09T13:03:23Z)
• A Simple "Motivation" Can Enhance Reinforcement Finetuning of Large Reasoning Models [103.88578274567784]
  Motivation-enhanced Reinforcement Finetuning (MeRF) is an intuitive yet effective method enhancing reinforcement finetuning of Large Reasoning Models.<n>MeRF directly injects the reward specification into the prompt, which serves as an in-context motivation for the model to be aware of the optimization objective.<n>MeRF achieves substantial performance gains over RLVR baseline.
  arXiv  Detail & Related papers  (2025-06-23T10:37:57Z)
• Estimating the Effects of Sample Training Orders for Large Language Models without Retraining [49.59675538160363]
  The order of training samples plays a crucial role in large language models (LLMs)<n>Traditional methods for investigating this effect generally require retraining the model with various sample orders.<n>We improve traditional methods by designing a retraining-free framework.
  arXiv  Detail & Related papers  (2025-05-28T07:07:02Z)
• Leveraging Importance Sampling to Detach Alignment Modules from Large Language Models [48.15777554876988]
  Traditional alignment methods often require retraining large pretrained models.<n>We propose a novel textitResidual Alignment Model (textitRAM) that formalizes the alignment process as a type of importance sampling.<n>We develop a resampling algorithm with iterative token-level decoding to address the common first-token latency issue in comparable methods.
  arXiv  Detail & Related papers  (2025-05-26T08:53:02Z)
• Insights from the Inverse: Reconstructing LLM Training Goals Through Inverse Reinforcement Learning [7.803423399566274]
  Large language models (LLMs) trained with Reinforcement Learning from Human Feedback have demonstrated remarkable capabilities, but their underlying reward functions and decision-making processes remain opaque.<n>This paper introduces a novel approach to interpreting LLMs by applying inverse reinforcement learning (IRL) to recover their implicit reward functions.<n>We conduct experiments on toxicity-aligned LLMs of varying sizes, extracting reward models that achieve up to 85% accuracy in predicting human preferences.
  arXiv  Detail & Related papers  (2024-10-16T12:14:25Z)
• DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System [83.34921966305804]
  Large language models (LLMs) have demonstrated remarkable performance in recommender systems.<n>We propose a novel plug-and-play alignment framework for LLMs and collaborative models.<n>Our method is superior to existing state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2024-08-15T15:56:23Z)
• CoMMIT: Coordinated Multimodal Instruction Tuning [90.1532838391285]
  Multimodal large language models (MLLMs) generally involve cooperative learning between a backbone LLM and a feature encoder of non-text input modalities.<n>In this paper, we analyze the MLLM instruction tuning from both theoretical and empirical perspectives.<n>We propose a Multimodal Balance Coefficient that enables quantitative measurement of the balance of learning.
  arXiv  Detail & Related papers  (2024-07-29T23:18:55Z)
• Graph-based Unsupervised Disentangled Representation Learning via Multimodal Large Language Models [42.17166746027585]
  We introduce a bidirectional weighted graph-based framework to learn factorized attributes and their interrelations within complex data. Specifically, we propose a $beta$-VAE based module to extract factors as the initial nodes of the graph. By integrating these complementary modules, our model successfully achieves fine-grained, practical and unsupervised disentanglement.
  arXiv  Detail & Related papers  (2024-07-26T15:32:21Z)
• Reinforcement Learning from Reflective Feedback (RLRF): Aligning and Improving LLMs via Fine-Grained Self-Reflection [24.435121488662897]
  We propose a novel framework: Reinforcement Learning from Reflective Feedback (RLRF) RLRF employs a self-reflection mechanism to systematically explore and refine LLM responses, then fine-tuning the models via a RL algorithm along with promising responses. Our experiments across Just-Eval, Factuality, and Mathematical Reasoning demonstrate the efficacy and transformative potential of RLRF.
  arXiv  Detail & Related papers  (2024-03-21T08:57:27Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)

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.