DelTA: Discriminative Token Credit Assignment for Reinforcement Learning from Verifiable Rewards
arXiv:2605.21467v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) has emerged as a central technique for improving the reasoning capabilities of large language models. Despite its effectiveness, how response-level rewards translate into token-level probability changes remains poorly understood. We introduce a discriminator view of RLVR updates, showing that the policy-gradient update direction implicitly acts as a linear discriminator over token-gradient vectors and thereby determines which token probabilities are increased or decreased during learning. Unde
The rapid advancement of large language models necessitates more refined and efficient alignment techniques, driving research into granular reward assignment methods like RLVR.
Understanding how RLVR updates translate to token-level behavior is crucial for developing more interpretable, controllable, and robust AI systems, impacting their safety and efficacy.
This research provides a deeper theoretical understanding of how reinforcement learning from verifiable rewards influences language model outputs at a foundational level, enabling more targeted development.
- · AI researchers
- · Large Language Model developers
- · AI safety and alignment organizations
- · Less granular RL techniques
- · AI systems prone to opaque decision-making
Improved methods for training large language models with greater precision and efficiency.
Development of AI systems that exhibit more predictable and verifiable reasoning capabilities.
Enhanced trust and broader integration of AI agents into critical applications requiring high levels of reliability.
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Read at arXiv cs.LG