Papers5

#In-Context Learning

Reinforcement Learning via Self-Distillation

Jonas Hübotter, Frederike Lübeck et al.Jan 28arXiv

The paper teaches large language models to learn from detailed feedback (like error messages) instead of only a simple pass/fail score.

#Self-Distillation#Reinforcement Learning with Rich Feedback#SDPO

Self-Distillation Enables Continual Learning

Intermediate

Idan Shenfeld, Mehul Damani et al.Jan 27arXiv

This paper shows a simple way for AI models to keep learning new things without forgetting what they already know.

#Self-Distillation Fine-Tuning#On-Policy Distillation#Continual Learning

Agentic Reasoning for Large Language Models

Intermediate

Tianxin Wei, Ting-Wei Li et al.Jan 18arXiv

This paper explains how to turn large language models (LLMs) from quiet students that only answer questions into active agents that can plan, act, and learn over time.

#Agentic Reasoning#LLM Agents#In-Context Learning

Nested Learning: The Illusion of Deep Learning Architectures

Intermediate

Ali Behrouz, Meisam Razaviyayn et al.Dec 31arXiv

The paper introduces Nested Learning, a new way to build AI that learns in layers (like Russian dolls), so each part can update at its own speed and remember different things.

#Nested Learning#Associative Memory#In-Context Learning

Meta-RL Induces Exploration in Language Agents

Intermediate

Yulun Jiang, Liangze Jiang et al.Dec 18arXiv

This paper introduces LAMER, a Meta-RL training framework that teaches language agents to explore first and then use what they learned to solve tasks faster.

#Meta-Reinforcement Learning#Language Agents#Exploration vs Exploitation