Agent-Omit: Adaptive Context Omission for Efficient LLM Agents
Agent-Omit enhances LLM agent efficiency by adaptively omitting context; experiments show comparable performance to frontier methods.
Key Findings
Methodology
Agent-Omit framework adaptively omits redundant thoughts and observations through synthesized cold-start data and an omit-aware agentic reinforcement learning approach. It employs a dual sampling mechanism and a tailored omission reward to incentivize omission capability, with theoretical guarantees on policy deviation bounded by KL-divergence.
Key Results
- Agent-Omit-8B excels across five benchmarks, achieving performance comparable to seven frontier LLM agents while surpassing seven efficient LLM methods in efficiency.
- In the WebShop environment, Agent-Omit-8B achieves a Pass@1 accuracy of 23.57% with an average token consumption of 8,764, significantly outperforming other methods.
- Experiments reveal the trained agent can adaptively omit 3-4 rounds of thought/observation, predominantly occurring in intermediate turns.
Significance
This research significantly improves LLM agent efficiency by adaptively omitting context, addressing the pain point of redundant information processing in multi-turn interactions. It offers new insights for efficient agent construction, with broad academic and industrial impact.
Technical Contribution
Agent-Omit introduces new theoretical guarantees and engineering possibilities by incorporating omit-aware reinforcement learning, significantly enhancing agent efficiency and providing theoretical bounds on omission strategy.
Novelty
This is the first framework to propose adaptive context omission, dynamically identifying and omitting redundant information compared to existing thought and observation management methods.
Limitations
- In complex tasks, omission may lead to information loss, affecting accuracy.
- The method relies on the quality of cold-start data, potentially impacting initial performance.
Future Work
Future directions include optimizing omission strategies in more complex interaction environments and integrating with other context management methods.
AI Executive Summary
The Agent-Omit framework enhances LLM agent efficiency by adaptively omitting context, addressing the pain point of redundant information processing in multi-turn interactions. Through synthesized cold-start data and an omit-aware agentic reinforcement learning approach, it adaptively omits redundant thoughts and observations. Experiments show that Agent-Omit-8B excels across five benchmarks, achieving performance comparable to seven frontier LLM agents while surpassing seven efficient LLM methods in efficiency. This research offers new insights for efficient agent construction, with broad academic and industrial impact. Future directions include optimizing omission strategies in more complex interaction environments and integrating with other context management methods.
Deep Analysis
Background
The rise of large language models (LLMs) in complex tasks has highlighted the need for efficiency improvements. Existing methods often struggle with redundant information in multi-turn interactions, leading to inefficiencies. Notable works like Kimi-K2 and DeepSeek-V3.2 have demonstrated capabilities across domains but still face context redundancy issues.
Core Problem
LLM agents often generate redundant thoughts and observations in multi-turn interactions, leading to inefficiencies. These redundancies increase computational costs and may affect task accuracy. Effective management of these redundancies is a pressing challenge.
Innovation
Agent-Omit improves LLM agent efficiency by adaptively omitting redundant information. Innovations include: 1) introducing omit-aware reinforcement learning to dynamically identify redundancies; 2) synthesizing cold-start data for optimized initial training; 3) providing theoretical bounds on omission strategy.
Methodology
- �� Synthesized cold-start data: Generate single-turn and multi-turn omission scenarios to fine-tune agents.
- �� Omit-aware reinforcement learning: Introduce dual sampling mechanism and tailored omission reward.
- �� Theoretical guarantees: Prove policy deviation bounds using KL-divergence.
Experiments
Experiments conducted across five benchmarks: DeepSearch, WebShop, TextCraft, BabyAI, and SciWorld. Monte Carlo rollouts evaluate omission strategy effectiveness, compared against seven frontier LLM agents. Key metrics include Pass@1 accuracy and token consumption.
Results
Agent-Omit-8B achieves a Pass@1 accuracy of 23.57% in the WebShop environment with an average token consumption of 8,764, significantly outperforming other methods. Experiments reveal the trained agent can adaptively omit 3-4 rounds of thought/observation, predominantly occurring in intermediate turns.
Applications
Agent-Omit can be applied to intelligent agent systems requiring efficient context management, such as automated customer service and intelligent search engines. Its adaptive omission mechanism significantly reduces computational costs and improves response speed.
Limitations & Outlook
While Agent-Omit performs well across environments, omission may lead to information loss in complex tasks, affecting accuracy. Additionally, the method relies on the quality of cold-start data, potentially impacting initial performance.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen, and Agent-Omit is like a smart assistant helping you decide which steps can be skipped. For example, if you already know how to stir-fry, the assistant suggests skipping repetitive steps to save time and effort. It's like having a smart helper that dynamically adjusts to your needs, ensuring you complete tasks in the shortest time possible.
ELI14 Explained like you're 14
Imagine you're playing a super complex game where you need to make constant decisions. Agent-Omit is like a smart game assistant that helps you skip unnecessary steps, letting you level up faster! It adjusts to the game's progress, ensuring you make the smartest choices at critical moments. Isn't that cool?
Glossary
LLM (Large Language Model)
A model capable of handling complex language tasks, typically with billions of parameters.
Used in this paper to build intelligent agents.
Context Omission
Dynamically omitting redundant information in multi-turn interactions to improve efficiency.
Core mechanism of the Agent-Omit framework.
Reinforcement Learning
A machine learning method that optimizes policies through interaction with the environment.
Used to train omit-aware agents.
KL-Divergence
A statistical measure of the difference between two probability distributions.
Used to theoretically prove the bounds of omission strategy.
Cold-Start Data
Dataset used to initialize model training, helping models quickly adapt to new tasks.
Used to fine-tune agent omission behavior.
Open Questions Unanswered questions from this research
- 1 Optimizing omission strategies in more complex environments remains an open research area.
- 2 The impact of cold-start data quality on omission strategies is not yet clear.
Applications
Immediate Applications
Intelligent Customer Service Systems
Enhance response speed and efficiency through adaptive omission mechanisms, reducing computational costs.
Long-term Vision
Intelligent Search Engines
Improve search efficiency and accuracy through dynamic context management, becoming industry standard.
Abstract
Managing agent context (e.g., thought and observation) during multi-turn agent-environment interactions is an emerging strategy to improve agent efficiency. However, existing studies treat the entire interaction trajectories equally, overlooking the thought necessity and observation utility varies across turns. To this end, we first conduct quantitative investigations into how thought and observation affect agent effectiveness and efficiency. Based on our findings, we propose Agent-Omit, a unified training framework that empowers LLM agents to adaptively omit redundant thoughts and observations. Specifically, we first synthesize a small amount of cold-start data, including both single-turn and multi-turn omission scenarios, to fine-tune the agent for omission behaviors. Furthermore, we introduce an omit-aware agentic reinforcement learning approach, incorporating a dual sampling mechanism and a tailored omission reward to incentivize the agent's adaptive omission capability. Theoretically, we prove that the deviation of our omission policy is upper-bounded by KL-divergence. Experimental results on five agent benchmarks show that our constructed Agent-Omit-8B could obtain performance comparable to seven frontier LLM agent, and achieve the best effectiveness-efficiency trade-off than seven efficient LLM agents methods. Our code and data are available at https://github.com/usail-hkust/Agent-Omit.