Enterprise AI Analysis
Agent-Omit: Training Efficient LLM Agents for Adaptive Thought and Observation Omission
This paper introduces Agent-Omit, a novel framework designed to enhance the efficiency and effectiveness of LLM agents by adaptively omitting redundant thoughts and observations during multi-turn interactions. It addresses the limitation of previous methods that treat entire interaction trajectories uniformly, failing to account for varying utility across turns. Agent-Omit utilizes an omit-behavior synthesis for cold-start fine-tuning and an omit-aware agentic reinforcement learning approach with a dual sampling mechanism and tailored omission reward. The framework is proven to bound omission policy deviation by KL-divergence. Experimental results on five benchmarks show Agent-Omit-8B achieves comparable accuracy to frontier LLMs while significantly reducing token cost, demonstrating superior effectiveness-efficiency trade-off.
Executive Impact at a Glance
Agent-Omit offers significant gains in LLM agent efficiency by intelligently managing context.
0
Avg Token Cost from Thought
0
Avg Token Cost from Observation
0
Avg Token Cost from Action
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Core Innovation
Technical Depth
Performance Benchmarks
Agent-Omit introduces a novel adaptive context management strategy for LLM agents, moving beyond static compression to dynamically omit redundant thoughts and observations. This significantly boosts efficiency without sacrificing performance.
The framework combines an 'Agent Omission Behavior Synthesis' for cold-start data generation and an 'Omit-Aware Agentic Reinforcement Learning' (RL) approach. The RL stage incorporates a dual sampling mechanism and a tailored omission reward, backed by theoretical proofs showing KL-divergence bounds on policy deviation.
Agent-Omit-8B achieved comparable accuracy to state-of-the-art LLMs (e.g., DeepSeek-R1-0528, Qwen3-32B) on five benchmarks (DeepSearch, WebShop, TextCraft, BabyAI, SciWorld) while drastically reducing token costs. It consistently outperformed seven efficient LLM agent construction methods in effectiveness-efficiency trade-offs, demonstrating adaptive omission of 3-4 rounds of context in intermediate turns.
0
Average Token Cost from Thought
0
Average Token Cost from Observation
Enterprise Process Flow
Omission Turn Identification
→
Hierarchical Omission Behavior Synthesis
→
Single-Turn Omission
→
Multi-Turn Omission
→
Omit-Aware Agentic Reinforcement Learning
| Method | Pass@1↑ | Avg Tok.↓ |
|---|---|---|
| Qwen3-8B Base | 6.93 | 16,741 |
| Thinking-Retention | 5.26 | 11,264 |
| DEPO | 10.26 | 10,286 |
| Tool-Light | 14.57 | 10,892 |
| Observation-Mask | 7.29 | 9,954 |
| DeepMiner | 6.82 | 11,367 |
| MEM-Agent | 6.62 | 10,011 |
| ReSum | 17.80 | 9,251 |
| Agent-Omit-8B-RL | 23.57 | 8,764 |
Adaptive Omission in DeepSearch Task
The Agent-Omit framework demonstrates its adaptive omission capabilities in knowledge-intensive tasks like DeepSearch. An example shows the agent initially thinking and calling a tool to search for 'The Affair season 4 episode count'. Instead of repeating detailed thought processes, it later generates an empty thought <think></think> and directly calls the tool for 'The Affair season 4 total episodes'. Furthermore, after multiple search attempts, it intelligently omits previous tool responses using <omit_tool_response_1></omit_tool_response_1> while refining its search query, showcasing dynamic context management and efficiency. This process leads to identifying the correct number of episodes (10) for 'The Affair season 4' with significantly reduced token usage.
Calculate Your Potential Savings with Agent-Omit
Estimate the efficiency gains and cost reductions for your enterprise by implementing Agent-Omit's adaptive LLM agent strategy.
Annual Cost Savings
$0
Hours Reclaimed Annually
0 Hours
Your Agent-Omit Implementation Roadmap
A phased approach to integrate adaptive LLM agents into your enterprise.
Phase 1: Discovery & Strategy
Assess current LLM agent usage, identify high-impact areas for optimization, and define custom omission policies based on enterprise-specific workflows.
Phase 2: Cold-Start Data Synthesis & Fine-Tuning
Generate synthetic omission behavior data and fine-tune your LLMs to learn the adaptive omission format and initial policy.
Phase 3: Omit-Aware Reinforcement Learning Integration
Deploy Agent-Omit in an agentic RL environment, leveraging dual sampling and tailored rewards to progressively refine omission capabilities and maximize efficiency.
Phase 4: Pilot Deployment & Optimization
Launch a pilot program with adaptive agents, monitor performance, gather feedback, and iterate on omission policies for continuous improvement and broader rollout.
Unlock Peak LLM Agent Efficiency
Revolutionize your enterprise's LLM agent operations. Schedule a personalized consultation to explore how Agent-Omit can reduce costs and boost performance.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat