Papers

Filtered to termination-token mismatch · clear filter

Browse by term

continual learning 86reinforcement learning 48benchmarking 13large language models 12benchmarks 11vision-language models 10language models 9robotics 7world models 7natural language processing 6recursive self-improvement 6generative models 5on-policy distillation 5video generation 5attention mechanisms 4coding agents 4LLMs 4multi-agent systems 4multimodal learning 4multimodal models 4self-distillation 4self-supervised learning 4transformers 4vision-language-action models 4world modeling 4agent-based systems 3agentic models 3agentic search 3agents 3autonomous systems 3

Matching papers

When EOS Tokens Disagree: Understanding Length Inflation in On-Policy Distillation

96 upvotes · 17 SEP 2026 · Yuxiao Yang, Tianrun Yu, Shangzhe Li et al.

This paper investigates why some AI models can generate excessively long responses and proposes a solution to mitigate this issue by aligning the models' termination tokens. Practitioners might care about this problem because it can lead to wasted generation budgets and inefficient AI applications.

on-policy distillationlength inflationEOS tokenstermination-token mismatchcontinual learning