Agent-Omit: Adaptive Context Omission for Efficient LLM Agents

Hao Liu; Jun Fang; Naiqiang Tan; Yansong Ning

arxiv: 2602.04284 · v2 · submitted 2026-02-04 · 💻 cs.AI · cs.LG

Agent-Omit: Adaptive Context Omission for Efficient LLM Agents

Yansong Ning , Jun Fang , Naiqiang Tan , Hao Liu This is my paper

Pith reviewed 2026-05-16 08:04 UTC · model grok-4.3

classification 💻 cs.AI cs.LG

keywords LLM agentscontext omissionreinforcement learningagent efficiencymulti-turn interactionsadaptive omissioncold-start data

0 comments

The pith

LLM agents can adaptively omit redundant thoughts and observations while matching frontier models with superior efficiency.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to establish that LLM agents can be trained to selectively skip unnecessary thoughts and observations during multi-turn interactions. It supports this through quantitative analysis of their varying impact on performance, followed by a training pipeline that starts with synthesized omission examples and refines via reinforcement learning. A sympathetic reader would care because full context retention drives up computational costs and limits how far agents can scale in extended tasks. If correct, the method shows that learned omission delivers comparable results to much larger models at lower resource use.

Core claim

Agent-Omit is a unified training framework that empowers LLM agents to adaptively omit redundant thoughts and observations. It first synthesizes cold-start data covering single-turn and multi-turn omission cases to fine-tune the agent, then applies an omit-aware agentic reinforcement learning stage with dual sampling and a tailored omission reward. The paper proves the resulting omission policy deviation is upper-bounded by KL-divergence. On five agent benchmarks the Agent-Omit-8B model reaches performance levels comparable to seven frontier LLM agents and records the best effectiveness-efficiency trade-off among seven efficient agent methods.

What carries the argument

The omit-aware agentic reinforcement learning stage with dual sampling and tailored omission reward, which trains the agent to decide when to omit thoughts or observations while keeping policy deviation bounded by KL-divergence.

If this is right

Agents maintain task performance on benchmarks while operating with shorter context windows.
An 8B model reaches effectiveness levels of much larger frontier agents through learned omission.
The method produces a better effectiveness-efficiency trade-off than prior efficient agent approaches.
Omission policies remain stable because their deviation from the original policy is provably bounded by KL-divergence.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same omission training could reduce token usage in non-agent sequential tasks such as extended dialogue or document reasoning.
Real-world agent deployments with variable task lengths might show even larger efficiency gains than those measured on fixed benchmarks.
The dual-sampling RL technique could transfer to other adaptive compression problems in long-context models.

Load-bearing premise

That the synthesized cold-start omission data and the tailored RL reward accurately reflect real necessity and utility of thoughts versus observations across the full range of agent tasks.

What would settle it

If the Agent-Omit-8B model is evaluated on the same five benchmarks and shows a clear drop in task success rates relative to frontier baselines while also failing to deliver measurable reductions in context length or compute.

Figures

Figures reproduced from arXiv: 2602.04284 by Hao Liu, Jun Fang, Naiqiang Tan, Yansong Ning.

**Figure 1.** Figure 1: Illustrative examples of how thought necessity and observation utility varies across turns. (a) Initial planning (e.g., search for Trivor and Muztagh Ata) already determines the subsequent tool call action, making follow-up thought redundant; (b) Observations from early turns are unuseful in the last turn, because only tool response in turn 4 is used for the answer summarization. ment observation (Su et … view at source ↗

**Figure 2.** Figure 2: Quantitative analysis of how thought and observation affect agent efficiency and effectiveness across interaction turns on WebShop environment using Qwen3-8B [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: The effect of thought and observation omission on agent efficiency and effectiveness across turns on WebShop environment using Qwen3-8B. The grey shaded region represents omitting at a specific turn could decrease token length without sacrificing accuracy. to task accuracy. The key findings, illustrated in [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

**Figure 4.** Figure 4: Overview of our proposed framework Agent-Omit. findings reveal a crucial insight: It is possible to reduce token costs without sacrificing accuracy, but only if the omission is applied selectively. However, because the optimal omission points are dynamic and task-dependent, a static heuristic is insufficient. This motivates the need for Agent-Omit, a framework that learns an adaptive policy to identify an… view at source ↗

**Figure 5.** Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

**Figure 6.** Figure 6: Statistics of average omission turns of Agent-Omit-8B-RL and its omission frequency across different turns. redundant thought and stacked environment observations), limiting agent efficiency. Many recent works have made efforts to address this, which can be categorized into three approaches: (1) Thought Management: Methods such as WebLeaper (Tao et al., 2025), DEPO, and ToolLight explicitly compress thoug… view at source ↗

**Figure 7.** Figure 7: The SFT training process visualization of Agent-Omit on five diverse domains. B.2. Agent Training Configuration Hyper-Parameter Setting. We summarize the hyper-parameter configurations of SFT and RL stage across five distinct environments in [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗

**Figure 8.** Figure 8: The RL training process visualization of Agent-Omit on WebShop using Qwen3-8B. C.1. SFT Training Visualization We analyze the training stability and convergence through the gradient norm and loss curves. As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Agent-Omit gives a workable training pipeline for an 8B agent to drop redundant thoughts and observations, but the gains rest on how faithfully the synthetic data and reward capture real necessity across tasks.

read the letter

Agent-Omit trains an 8B model to drop unnecessary thoughts and observations during agent runs, and the results suggest it can match bigger models on performance while cutting context costs. The main new piece is the two-stage setup: first synthesize a bit of cold-start data for omission in single and multi-turn cases, fine-tune on that, then run omit-aware RL with dual sampling and a reward that encourages safe omissions. They start with some quantitative checks on how thoughts and observations actually matter across turns, which grounds the design. That approach looks practical. The benchmarks on five agent tasks show the 8B version staying competitive with seven frontier agents and beating seven other efficient methods on the effectiveness-efficiency curve. Having the code out helps too. The soft spot is the reliance on that synthesized data and the custom reward. If the synthetic scenarios miss the cases where omitting something looks fine but actually hurts later turns, or if the reward doesn't penalize bad omissions strongly enough, the policy could quietly lose performance on new tasks. The KL bound they prove is the standard one from policy optimization, so it doesn't add much new protection. The abstract mentions quantitative investigations, but without the full stats and splits it's tough to see how solid the motivation is. This paper is aimed at researchers building multi-turn LLM agents who care about token usage and latency. Anyone trying to deploy agents without scaling up the model size would find the method and the open repo useful. It has enough empirical grounding and a clear method to warrant a full review, even if the data synthesis needs close scrutiny. I'd recommend sending it to referees.

Referee Report

3 major / 2 minor

Summary. The paper proposes Agent-Omit, a unified training framework for LLM agents that adaptively omits redundant thoughts and observations during multi-turn interactions. It first performs quantitative investigations into thought necessity and observation utility, synthesizes cold-start data for single- and multi-turn omission scenarios to fine-tune the base model, applies an omit-aware agentic RL stage with dual sampling and a tailored reward, proves a KL-divergence upper bound on policy deviation, and reports that the resulting Agent-Omit-8B model matches the performance of seven frontier LLM agents while achieving the best effectiveness-efficiency trade-off among seven efficient baselines across five agent benchmarks.

Significance. If the empirical results hold under rigorous verification, the work would be significant for efficient LLM agent design by demonstrating that adaptive context omission can close the gap to much larger models while improving token efficiency. The release of code and data is a positive factor for reproducibility. The theoretical KL bound, however, is a standard RL result and does not constitute a novel contribution.

major comments (3)

[§4.2] §4.2 (omit-aware RL): the tailored omission reward and dual-sampling mechanism are described at a high level but lack the explicit functional form or weighting between omission gain and performance penalty; without this, it is impossible to verify whether the reward correctly penalizes harmful omissions on out-of-distribution turns, which is load-bearing for the headline benchmark claims.
[§3] §3 (quantitative investigations and cold-start synthesis): the procedure for labeling necessity/utility in the synthesized single- and multi-turn omission data is not detailed (e.g., how ground-truth omission decisions are obtained and how data splits avoid overfitting to the investigated regimes), undermining confidence that the fine-tuning stage generalizes to the five evaluation benchmarks.
[Results section] Results section (Tables reporting Agent-Omit-8B vs. seven frontier and seven efficient baselines): the paper must report per-benchmark scores, standard deviations across runs, and exact model sizes/context lengths for all compared systems; the current aggregate claim of “comparable performance” and “best trade-off” cannot be assessed without these numbers.

minor comments (2)

[Abstract] Abstract: 'seven frontier LLM agent' should read 'seven frontier LLM agents'.
[Methods] Notation: the distinction between 'thought' and 'observation' tokens is used throughout but never given an explicit token-level definition or example in the methods.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major comment below and will revise the manuscript to incorporate the requested clarifications and data.

read point-by-point responses

Referee: [§4.2] §4.2 (omit-aware RL): the tailored omission reward and dual-sampling mechanism are described at a high level but lack the explicit functional form or weighting between omission gain and performance penalty; without this, it is impossible to verify whether the reward correctly penalizes harmful omissions on out-of-distribution turns, which is load-bearing for the headline benchmark claims.

Authors: We agree that the explicit functional forms and weighting are essential for verification. In the revised manuscript, we will add the precise mathematical definition of the tailored omission reward (including the weighting coefficient between omission gain and performance penalty) and provide equations plus pseudocode for the dual-sampling mechanism. These additions will explicitly demonstrate how the reward penalizes harmful omissions on out-of-distribution turns. revision: yes
Referee: [§3] §3 (quantitative investigations and cold-start synthesis): the procedure for labeling necessity/utility in the synthesized single- and multi-turn omission data is not detailed (e.g., how ground-truth omission decisions are obtained and how data splits avoid overfitting to the investigated regimes), undermining confidence that the fine-tuning stage generalizes to the five evaluation benchmarks.

Authors: We will expand Section 3 with a complete description of the labeling procedure, including how ground-truth omission decisions are obtained (via oracle simulation on held-out trajectories) and the data-splitting strategy used to avoid overfitting to the investigated regimes. This will strengthen the claim that the cold-start fine-tuning generalizes to the five evaluation benchmarks. revision: yes
Referee: Results section (Tables reporting Agent-Omit-8B vs. seven frontier and seven efficient baselines): the paper must report per-benchmark scores, standard deviations across runs, and exact model sizes/context lengths for all compared systems; the current aggregate claim of “comparable performance” and “best trade-off” cannot be assessed without these numbers.

Authors: We acknowledge this requirement. In the revised results section and tables, we will report per-benchmark scores with standard deviations across multiple runs and list the exact model sizes and context lengths for all compared frontier and efficient baselines. This will allow direct assessment of the performance and efficiency claims. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained via standard RL bounds and external benchmarks

full rationale

The paper's chain begins with quantitative investigations into thought/observation necessity, proceeds to synthesis of cold-start omission data, applies omit-aware RL with dual sampling and tailored reward, and invokes a standard KL-divergence upper bound on policy deviation. None of these steps reduce by construction to fitted parameters defined inside the paper or to self-citations whose content is unverified. Performance claims are measured against external frontier models and baselines on five separate benchmarks, and the KL result is explicitly identified as a standard result from RL literature rather than a paper-specific derivation. This satisfies the criteria for a self-contained, non-circular derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are explicitly introduced beyond standard RL components and synthesized training data.

pith-pipeline@v0.9.0 · 5517 in / 980 out tokens · 34016 ms · 2026-05-16T08:04:07.044098+00:00 · methodology

discussion (0)

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Rethinking Agentic Reinforcement Learning In Large Language Models
cs.AI 2026-04 unverdicted novelty 3.0

The paper reviews conceptual foundations, methodological innovations, effective designs, critical challenges, and future directions for LLM-based Agentic Reinforcement Learning.
Rethinking Agentic Reinforcement Learning In Large Language Models
cs.AI 2026-04 unverdicted novelty 2.0

This review synthesizes conceptual foundations, methods, challenges, and future directions for agentic reinforcement learning in large language models.
Rethinking Agentic Reinforcement Learning In Large Language Models
cs.AI 2026-04 unverdicted novelty 2.0

The paper surveys the conceptual foundations, methodological innovations, challenges, and future directions of agentic reinforcement learning frameworks that embed cognitive capabilities like meta-reasoning and self-r...

Reference graph

Works this paper leans on

16 extracted references · 16 canonical work pages · cited by 1 Pith paper

[1]

Do not generate <answer> before receiving a corresponding <tool response>, unless you are fully confident that no external tool invocation is required

work page
[2]

This procedure should be followed consistently

If no further external knowledge is needed, you may directly provide the final answer enclosed by<answer> and </answer>, without detailed intermediate explanations (e.g., <answer> Beijing </answer>). This procedure should be followed consistently

work page
[3]

The use of <think> </think> is encouraged to preserve relevant context when you are confident about the next action

work page
[4]

Let us begin

The tokens <omit tool response N ...> </omit tool response N ...> can be used to omit tool responses at turn N for context compression, and are recommended when the interaction involves many turns or becomes stuck at a particular step. Let us begin. Remember to invoke <think> </think> or <omit tool response N ...> </omit tool response N ...>whenever neces...

work page 2025
[5]

An action should be wrapped in ” <tool call>...</tool call>”, and the action content should be the following structure: search[keywords] or click[value]

work page
[6]

Keywords in search are up to you, but the value in click must be a value in the list of available actions

If the action is not valid, perform nothing. Keywords in search are up to you, but the value in click must be a value in the list of available actions

work page
[7]

Remember that your keywords in search should be carefully designed

work page
[9]

Let us begin

” <omit tool response N ...></omit tool response N ...>” can help you save context by omitting prior tool responses at turn N, you are encouraged to use when there have too many turns or are clearly stuck on a given step. Let us begin. Remember to invoke <think> </think> or <omit tool response N ...> </omit tool response N ...>whenever necessary to save c...

work page 2024
[10]

Always specify the quantity when using ”get” and ”craft” commands. - Example of get:<tool call>get 1 lapis lazuli</tool call> - Example1 of craft: <tool call>craft 1 blue dye using 1 lapis lazuli</tool call> - Example2 of craft:<tool call>craft 1 golden carrot using 8 gold nugget, 1 carrot</tool call>

work page
[11]

When using ”get” command, do not specify whether the item comes from the inventory or the environment

work page
[12]

However, if the crafting command uses a generic ingredient like ”planks”, you can use special types of the same ingredient e.g

You can use ONLY crafting commands provided, do not use your own crafting commands. However, if the crafting command uses a generic ingredient like ”planks”, you can use special types of the same ingredient e.g. ”dark oak planks” in the command instead

work page
[13]

”<think></think>” is a good way to save context when you are confident about your next action

work page
[14]

Let us begin

”<omit tool response N ...></omit tool response N ...>” can help you save context by omit- ting prior tool responses at turn N, you are encouraged to use when there have too many turns or are clearly stuck on a given step. Let us begin. Remember to invoke <think> </think> or <omit tool response N ...> </omit tool response N ...>whenever necessary to save ...

work page 2018
[15]

2.Only when task is finished can you provide final answer

You should put your action in<tool call>...</tool call>. 2.Only when task is finished can you provide final answer

work page
[16]

<think></think>

“<think></think>” is a good way to save context when you are confident about your next action. 4.“<omit tool response N ...></omit tool response N ...>” can help you save context by omitting prior tool responses at turn N, you are encouraged to use when there have too many turns or are clearly stuck on a given step. Let us begin. Remember to invoke <think...

work page 2022
[17]

role": "system

An action should be wrapped in <tool call>...</tool call>, and the action must be chosen from the given functions. The objects you choose must exist in the current room. Any actions except provided available actions will be regarded as illegal. 2. Think when necessary, try to act directly more in the process. 3. After your each turn, the environment will ...

work page

[1] [1]

Do not generate <answer> before receiving a corresponding <tool response>, unless you are fully confident that no external tool invocation is required

work page

[2] [2]

This procedure should be followed consistently

If no further external knowledge is needed, you may directly provide the final answer enclosed by<answer> and </answer>, without detailed intermediate explanations (e.g., <answer> Beijing </answer>). This procedure should be followed consistently

work page

[3] [3]

The use of <think> </think> is encouraged to preserve relevant context when you are confident about the next action

work page

[4] [4]

Let us begin

The tokens <omit tool response N ...> </omit tool response N ...> can be used to omit tool responses at turn N for context compression, and are recommended when the interaction involves many turns or becomes stuck at a particular step. Let us begin. Remember to invoke <think> </think> or <omit tool response N ...> </omit tool response N ...>whenever neces...

work page 2025

[5] [5]

An action should be wrapped in ” <tool call>...</tool call>”, and the action content should be the following structure: search[keywords] or click[value]

work page

[6] [6]

Keywords in search are up to you, but the value in click must be a value in the list of available actions

If the action is not valid, perform nothing. Keywords in search are up to you, but the value in click must be a value in the list of available actions

work page

[7] [7]

Remember that your keywords in search should be carefully designed

work page

[8] [9]

Let us begin

” <omit tool response N ...></omit tool response N ...>” can help you save context by omitting prior tool responses at turn N, you are encouraged to use when there have too many turns or are clearly stuck on a given step. Let us begin. Remember to invoke <think> </think> or <omit tool response N ...> </omit tool response N ...>whenever necessary to save c...

work page 2024

[9] [10]

Always specify the quantity when using ”get” and ”craft” commands. - Example of get:<tool call>get 1 lapis lazuli</tool call> - Example1 of craft: <tool call>craft 1 blue dye using 1 lapis lazuli</tool call> - Example2 of craft:<tool call>craft 1 golden carrot using 8 gold nugget, 1 carrot</tool call>

work page

[10] [11]

When using ”get” command, do not specify whether the item comes from the inventory or the environment

work page

[11] [12]

However, if the crafting command uses a generic ingredient like ”planks”, you can use special types of the same ingredient e.g

You can use ONLY crafting commands provided, do not use your own crafting commands. However, if the crafting command uses a generic ingredient like ”planks”, you can use special types of the same ingredient e.g. ”dark oak planks” in the command instead

work page

[12] [13]

”<think></think>” is a good way to save context when you are confident about your next action

work page

[13] [14]

Let us begin

”<omit tool response N ...></omit tool response N ...>” can help you save context by omit- ting prior tool responses at turn N, you are encouraged to use when there have too many turns or are clearly stuck on a given step. Let us begin. Remember to invoke <think> </think> or <omit tool response N ...> </omit tool response N ...>whenever necessary to save ...

work page 2018

[14] [15]

2.Only when task is finished can you provide final answer

You should put your action in<tool call>...</tool call>. 2.Only when task is finished can you provide final answer

work page

[15] [16]

<think></think>

“<think></think>” is a good way to save context when you are confident about your next action. 4.“<omit tool response N ...></omit tool response N ...>” can help you save context by omitting prior tool responses at turn N, you are encouraged to use when there have too many turns or are clearly stuck on a given step. Let us begin. Remember to invoke <think...

work page 2022

[16] [17]

role": "system

An action should be wrapped in <tool call>...</tool call>, and the action must be chosen from the given functions. The objects you choose must exist in the current room. Any actions except provided available actions will be regarded as illegal. 2. Think when necessary, try to act directly more in the process. 3. After your each turn, the environment will ...

work page