🔗 Source: arXiv

Test-Time Learning for Large Language Models

🚀 Technical Novelty

  • Mechanism: Formulates test-time updates as input perplexity minimization, guided by a sample-efficient strategy that prioritizes high-perplexity samples and applies lightweight LoRA updates.
  • Nuance: Replaces prior TTA methods’ reliance on output entropy minimization with autoregressive-aware input perplexity optimization, preventing degradation of generative dynamics while mitigating catastrophic forgetting through parameter-efficient fine-tuning.

💡 Yield

  • Delivers ≥20% relative performance gains over base LLMs and outperforms SOTA TTA baselines (Tent, EATA, COME) across domain knowledge, instruction-following, and reasoning benchmarks; cuts online backward passes by ~70% via selective sample weighting.

⚠️ Limitations

  • Adaptation performance is sensitive to the perplexity threshold hyperparameter (P0); greedy decoding is enforced during updates which may limit response diversity; and online stability requires batching updates every 100 samples rather than per-step.