Internalizing Outcome Supervision into Process Supervision: A New Paradigm for Reinforcement Learning for Reasoning
arXiv:2605.05226v2 Announce Type: replace-cross Abstract: The central challenge of reinforcement learning for reasoning lies not only in the sparsity of outcome-level supervision, but more fundamentally in how to transform feedback provided only at the end of a sequence into fine-grained learning signals that can guide intermediate reasoning steps. Existing approaches either rely on outcome-level rewards for sequence-level optimization, which makes precise credit assignment difficult, or depend on externally constructed process supervision, which is costly and difficult to scale sustainably. T
The continuous evolution of AI models and reasoning capabilities necessitates more efficient and scalable training methods to overcome prior limitations in reinforcement learning.
Improving reinforcement learning for reasoning by internalizing outcome supervision into process supervision could significantly accelerate the development of more capable and autonomous AI systems.
This new paradigm shifts from costly external process supervision or inefficient outcome-level rewards to more fine-grained, self-generated learning signals, making complex AI training more scalable.
- · AI research institutions
- · Companies developing advanced AI agents
- · Generative AI platforms
- · Companies relying on manual data labeling for process supervision
- · AI models constrained by sparse outcome-only feedback
More sophisticated AI models with enhanced reasoning abilities will emerge.
The cost and complexity of training highly intelligent AI systems may decrease, broadening access to advanced AI.
This could accelerate the deployment of autonomous AI agents across various sectors, leading to significant disruption of white-collar workflows.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL