Papers with 7B policy model

    1 papers
    Crossing the Reward Bridge: Expanding Reinforcement Learning with Verifiable Rewards Across Diverse Domains (2026.acl-long)

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    Challenge: Reinforcement learning with verifiable rewards (RLVR) has been effective on structured tasks, but its reliance on simple, rule-based verifiers creates a bottleneck.
    Approach: They propose a framework that uses a generative verifier to provide soft, probabilistic rewards.
    Outcome: The proposed framework outperforms existing models up to 10x their size and can be scalable and effective.

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