Learning from Own Solutions: Self-Conditioned Credit Assignment for Reinforcement Learning with Verifiable Rewards
arXiv:2606.18810v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has driven substantial progress in training LLMs for reasoning tasks, but representative methods such as GRPO assign uniform credit across all tokens, wasting gradient on routine tokens while under-crediting pivotal reasoning steps. Existing token-level credit assignment methods require resources beyond the model's own rollouts. GRPO variants rely on process reward models or ground-truth answers. Knowledge distillation assigns credit through per-token divergence but requires external teacher
The paper addresses a critical limitation in current LLM training paradigms, specifically inefficient credit assignment in Reinforcement Learning with Verifiable Rewards (RLVR), a dominant method for reasoning tasks.
Improving credit assignment for LLMs in reasoning tasks directly enhances their performance and efficiency, accelerating progress in autonomous AI agents and complex problem-solving.
This new method reduces reliance on external resources for credit assignment in RLVR, making LLM training more self-sufficient and potentially scalable for complex reasoning.
- · AI model developers
- · LLM-powered agentic systems
- · Companies investing in AI research
- · Developers of reasoning-intensive AI applications
- · Previous credit assignment methodologies
- · External data providers for RLVR supervision
- · Organizations relying on less efficient LLM training
More efficient and capable LLMs for reasoning-heavy tasks emerge, improving their ability to solve complex problems.
The cost and complexity of training highly capable LLMs decrease, democratizing access to advanced AI capabilities.
Accelerated development of robust and autonomous AI agents capable of collapsing white-collar workflows at scale.
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Read at arXiv cs.AI