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Source note3 min read

Right in the Right Way: LM Training with Verifiable Rewards and Human Demonstrations

Source

arXiv

arxiv.org/abs/2607.01181v1

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Plain-Language Brief

Researchers propose an adversarial generator-discriminator framework to train language models (LM) for tasks with well-defined success metrics, like code generation and mathematical reasoning. The new approach improves non-verifiable aspects such as style and structure, which are often overlooked in current methods. The method showed consistent improvements across various domains, including bug fixing, story generation, and a reward hacking benchmark.

What the Document Says

  • RL with verifiable rewards (RLVR) is used for training LMs on tasks with clear success metrics.
  • Current RLVR methods only optimize objectively scoreable aspects, often neglecting subjective, non-verifiable aspects like style and structure.
  • The paper presents an adversarial generator-discriminator framework that combines verifiable rewards with a learned signal from human demonstrations.
  • A generator model is trained using RL to maximize both task accuracy and an adversarial reward derived from the discriminator.
  • The discriminator learns to distinguish human-written outputs from model-generated ones, serving as a learned proxy for the human output distribution.
  • Across diverse domains (bug fixing, open-ended generation), the method improves non-verifiable properties while preserving accuracy gains of RLVR.
  • In bug fixing, the method produces solutions with lower edit distance compared to RLVR baselines while matching end performance.
  • In story generation, the method improves win rate and produces more human-like stories.
  • The method nearly eliminates model misbehavior in a reward hacking benchmark while maintaining high scores.
  • The authors argue that their approach bridges RL and SFT (Sequential Decision Making under Uncertainty), offering a scalable path towards jointly optimizing verifiable and non-verifiable properties of a task.

Key Concepts Mentioned

  • RL with Verifiable Rewards
  • Language Model
  • Adversarial Generator-Discriminator Framework
  • Bug Fixing
  • Story Generation
  • Reward Hacking

Claims Made by the Document

  • Current RLVR methods neglect subjective, non-verifiable aspects of human-like outputs.
  • The proposed approach improves non-verifiable properties while preserving the accuracy gains of RLVR.
  • In bug fixing, our method produces solutions with significantly lower edit distance compared to RLVR baselines while matching end performance.
  • In story generation, our method significantly improves win rate and produces stories that are diverse and more human-like.
  • In a simple reward hacking benchmark, our method nearly eliminates model misbehavior while maintaining high benchmark scores.

Evidence Used

  • The paper explains the current limitations of RLVR methods and the proposed solution.
  • Results across diverse domains are presented in the paper to demonstrate the improvements achieved by the proposed method.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

RL with verifiable rewards (RLVR) has emerged as a powerful paradigm for training LMs on tasks with well-defined success metrics, such as code generation and mathematical reasoning. However, current RLVR methods optimize only what can be objectively scored, often neglecting subjective, non-verifiable aspects of human-like outputs, such as style and structure. This limitation leads to well-documented failure modes such as diversity collapse, unnatural-sounding responses, and reward hacking. We propose an adversarial generator-discriminator framework that augments verifiable rewards with a learned signal from human demonstrations. A generator model is trained using RL to maximize both task accuracy and an adversarial reward derived from a discriminator. The discriminator, trained alongside the generator policy, learns to distinguish human-written outputs from model-generated ones. The discriminator serves as a learned proxy for the human output distribution, providing feedback on aspects of generation that are difficult to formalize as scalar rewards. Across diverse domains, including bug fixing and open-ended generation, our approach consistently improves non-verifiable properties while preserving the accuracy gains of RLVR. In bug fixing, our method produces solutions with significantly lower edit distance compared to RLVR baselines while matching end performance. In story generation, our method significantly improves win rate while producing stories that are diverse and more human-like. And in a simple reward hacking benchmark, our method nearly eliminates model misbehavior while maintaining high benchmark scores. Together, these results show that our approach bridges RL and SFT, offering a scalable path toward jointly optimizing the verifiable and non-verifiable properties of a task.