Observation-Calibrated Self-Distillation for LLM Agents
A novel technique known as Observation-Calibrated Self-Distillation (OCSD) has been introduced to enhance the training process of large language model (LLM) agents. This method tackles a complex challenge found in On-Policy Self-Distillation (OPSD), which involves re-evaluating generated tokens through a privileged replay view to achieve detailed token-level guidance. The challenge arises because OPSD's support may represent both the privileged data in the replay view and score alterations caused by the replay scaffold, complicating the attribution of support to specific information. This issue is particularly significant when future environmental observations act as privileged data, necessitating the reconstruction of an extended scaffold that disrupts token scores. OCSD addresses this by comparing two structurally similar scaffolds to clarify the influence of the privileged information. The research is accessible on arXiv with the identifier 2608.04788 and was announced as a cross-type submission.
Key facts
- OCSD is proposed to resolve confounding in OPSD for LLM agents.
- OPSD uses privileged replay views to provide dense token-level supervision.
- The confounding issue arises from score shifts induced by the replay scaffold.
- Future environment observations as privileged information exacerbate the issue.
- OCSD contrasts two structurally matched scaffolds.
- The paper is available on arXiv with ID 2608.04788.
- The announcement type is 'cross'.
- The method aims to improve reinforcement learning with sparse trajectory-level rewards.
Entities
Institutions
- arXiv