PCSD: Persistent Consistency for Self-Distillation in Agentic RL
A recent study published on arXiv (2608.01837) presents Persistent Consistency Self-Distillation (PCSD), a novel technique designed to enhance reinforcement learning for large language model agents. This method tackles the issue of sparse rewards in lengthy multi-turn interactions by offering dense supervision at the token level through on-policy self-distillation (OPSD). However, OPSD can be unreliable at specific points, and current approaches either focus on isolated token-level differences (sensitive to noise) or apply uniform step-level weights (ignoring positional changes). PCSD generates token-level distillation weights based on the local persistence of signals favoring the teacher, utilizing adaptive windows and exponentially decayed aggregation to maintain consistent relative teacher support. Additionally, it employs trend-aware modulation to reduce locally diminishing support, resulting in continuous weights. This method seeks to improve the stability and efficacy of RL for language agents engaged in intricate interactive tasks.
Key facts
- Paper arXiv:2608.01837 introduces PCSD (Persistent Consistency Self-Distillation).
- PCSD addresses sparse rewards in reinforcement learning for large language model agents.
- On-policy self-distillation (OPSD) provides dense token-level supervision from a privileged teacher.
- Existing methods rely on isolated token-level discrepancies or shared step-level weights.
- PCSD derives token-level distillation weights from local persistence of teacher-favoring signals.
- It combines adaptive windows with exponentially decayed aggregation.
- Trend-aware modulation attenuates locally declining support.
- PCSD produces continuous weights for distillation.
Entities
Institutions
- arXiv