From Verifiable Dot to Reward Chain: Harnessing Verifiable Reference-based Rewards for Reinforcement Learning of Open-ended Generation

2026-01-26

AI Summary

This work addresses the inefficiency and vulnerability to reward hacking in existing verifiable-reward-based reinforcement learning approaches for open-ended generation tasks, where ground-truth answers are often unavailable. The authors propose RLVRR, a novel method that extends verifiable rewards from single-point answers to a “reward chain” derived from high-quality reference texts. This framework establishes a dual-track verifiable reward mechanism by evaluating both content fidelity (via keyword retention) and stylistic quality (through large language model validation). By integrating the strengths of reinforcement learning and supervised fine-tuning, RLVRR unifies structured reasoning with open-ended generation in a single training paradigm. Extensive experiments across more than ten benchmarks demonstrate that RLVRR significantly outperforms supervised fine-tuning with ten times more data and state-of-the-art reward models, consistently improving generation quality, generalization, and output diversity.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation
• Computer Vision: Diffusion Models for Vision

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Reinforcement learning with verifiable rewards (RLVR) succeeds in reasoning tasks (e.g., math and code) by checking the final verifiable answer (i.e., a verifiable dot signal). However, extending this paradigm to open-ended generation is challenging because there is no unambiguous ground truth. Relying on single-dot supervision often leads to inefficiency and reward hacking. To address these issues, we propose reinforcement learning with verifiable reference-based rewards (RLVRR). Instead of checking the final answer, RLVRR extracts an ordered linguistic signal from high-quality references (i.e, reward chain). Specifically, RLVRR decomposes rewards into two dimensions: content, which preserves deterministic core concepts (e.g., keywords), and style, which evaluates adherence to stylistic properties through LLM-based verification. In this way, RLVRR combines the exploratory strength of RL with the efficiency and reliability of supervised fine-tuning (SFT). Extensive experiments on more than 10 benchmarks with Qwen and Llama models confirm the advantages of our approach. RLVRR (1) substantially outperforms SFT trained with ten times more data and advanced reward models, (2) unifies the training of structured reasoning and open-ended generation, and (3) generalizes more effectively while preserving output diversity. These results establish RLVRR as a principled and efficient path toward verifiable reinforcement learning for general-purpose LLM alignment. We release our code and data at https://github.com/YJiangcm/RLVRR.

Problem

• reinforcement learning
• open-ended generation
• verifiable rewards
• reward hacking
• large language models

Innovation

• reward chain
• verifiable reference-based rewards
• reinforcement learning
• open-ended generation
• LLM alignment