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arxiv: 2605.05413 · v1 · submitted 2026-05-06 · 💻 cs.AI

Recognition: unknown

From History to State: Constant-Context Skill Learning for LLM Agents

Feng Ju, Haoyang Xie, Puda Zhao, Xinyuan Wang, Yancheng Wang

Authors on Pith no claims yet

Pith reviewed 2026-05-08 16:45 UTC · model grok-4.3

classification 💻 cs.AI
keywords LLM agentsconstant contextskill learningtask-family modulesstate trackingALFWorldWebShopSciWorld
0
0 comments X

The pith

LLM agents replace growing prompt histories with compact state blocks and learned task-family modules to cut context length while matching strong benchmark results.

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

The paper establishes that recurring agent workflows can be partitioned into reusable task-family modules whose procedural knowledge is absorbed into model weights. At inference time the model conditions only on the current observation plus a small deterministic state block that tracks progress and supplies subgoal rewards, enabling step-level supervised fine-tuning followed by online reinforcement learning. This yields 89.6% unseen success on ALFWorld, 76.8% on WebShop, and 66.4% unseen success on SciWorld with Qwen3-8B, matching or exceeding prior agent-training results while using 2-7 times fewer prompt tokens per turn than ReAct baselines. A sympathetic reader would care because the approach simultaneously improves privacy, reduces cost, and maintains capability for personal assistants that operate browsers, files, and tools.

Core claim

Reusable procedures for agent tasks are learned into lightweight task-family modules; inference uses only the current observation and a compact state block produced by a deterministic tracker that also supplies aligned subgoal rewards, so each module trains via step-level SFT and is refined by online RL. With this constant-context regime, Qwen3-8B reaches 89.6% unseen success on ALFWorld, 76.8% success on WebShop, and 66.4% unseen success on SciWorld while cutting prompt tokens per turn by 2-7x relative to controlled ReAct prompting.

What carries the argument

Constant-context skill learning via task-family modules conditioned on current observation and a compact state block rendered by a deterministic progress tracker.

If this is right

  • Agents maintain high task success without accumulating full interaction histories in every prompt.
  • SFT followed by RL using tracker-supplied subgoal rewards refines each module for the target task family.
  • The same approach works across base models including Qwen3-4B, Qwen3-8B, and Llama-3.1-8B.
  • Token usage per turn drops by factors of 2-7 relative to standard history-based prompting on the tested benchmarks.

Where Pith is reading between the lines

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

  • Local deployment becomes more attractive because far less intermediate context leaves the device.
  • Many everyday agent tasks appear to possess modular structure that can be pre-learned once and reused.
  • The method may scale to domains where workflows are harder to partition if richer state representations are developed.

Load-bearing premise

Recurring agent workflows can be cleanly partitioned into task-family modules whose compact state block captures all necessary history and progress information without loss of critical context.

What would settle it

Replace the state block with an empty or randomly generated block on the same ALFWorld, WebShop, and SciWorld tasks and measure whether success rates fall substantially below the reported 89.6%, 76.8%, and 66.4% figures.

Figures

Figures reproduced from arXiv: 2605.05413 by Feng Ju, Haoyang Xie, Puda Zhao, Xinyuan Wang, Yancheng Wang.

Figure 1
Figure 1. Figure 1: Context-to-weights skill learning pipeline. view at source ↗
Figure 2
Figure 2. Figure 2: Qualitative ALFWorld household trace with rendered state blocks. view at source ↗
Figure 3
Figure 3. Figure 3: Reward ablation. Using only terminal task reward during RL drops success by 4.6 points, showing that subgoal rewards provide useful inter￾mediate feedback view at source ↗
Figure 4
Figure 4. Figure 4: WebShop SFT performance versus expert trajectory count. view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative WebShop shopping trace with rendered state blocks. view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative SciWorld lab-procedure trace with rendered state blocks. view at source ↗
read the original abstract

Large language model (LLM) agents are increasingly used to operate browsers, files, code and tools, making personal assistants a natural deployment target. Yet personal agents face a privacy-cost-capability tension: cloud models execute multi-step workflows well but expose sensitive intermediate context to external APIs, while local models preserve privacy but remain less reliable. Both settings also pay repeatedly for long skill prompts and growing histories. We propose constant-context skill learning, a context-to-weights framework for recurring agent workflows: reusable procedures are learned in lightweight task-family modules, while inference conditions only on the current observation and a compact state block. A deterministic tracker renders this state block from task progress and supplies aligned subgoal rewards, so each module can be trained with step-level SFT and refined through online RL. Across ALFWorld, WebShop, and SciWorld, our agents achieve strong performance across Qwen3-4B, Qwen3-8B and Llama-3.1-8B. With Qwen3-8B, SFT+RL reaches 89.6\% unseen success on ALFWorld, 76.8\% success on WebShop, and 66.4\% unseen success on SciWorld. They match or exceed strong published agent-training results while reducing prompt tokens per turn by 2--7$\times$ relative to controlled ReAct prompting baselines, showing that procedural context can be moved from prompts into weights.

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.

Referee Report

2 major / 1 minor

Summary. The paper proposes constant-context skill learning for LLM agents: recurring workflows are partitioned into lightweight task-family modules whose procedural history is moved into weights via SFT+RL training; at inference only the current observation and a compact state block (produced by a deterministic tracker that also supplies subgoal rewards) are provided. On ALFWorld, WebShop and SciWorld the method reports 89.6% unseen success (Qwen3-8B), 76.8% success and 66.4% unseen success respectively, matching or exceeding prior agent-training results while cutting prompt tokens per turn by 2–7× versus controlled ReAct baselines.

Significance. If the central claim holds, the work offers a practical route to privacy-preserving and token-efficient local agents by embedding reusable procedures in model weights rather than long prompts. The reported numbers on three distinct environments and multiple base models constitute a concrete empirical contribution, though their interpretability depends on the missing controls noted below.

major comments (2)
  1. [Methods (tracker and state-block construction)] The central claim that the deterministic tracker’s compact state block is informationally complete for unseen tasks is load-bearing, yet no ablations are described that remove or corrupt individual elements (subgoal progress flags, observation summaries, etc.) and measure the resulting drop in success rate or change in failure modes on held-out tasks. Without such evidence it remains possible that the 89.6% ALFWorld figure relies on environment-specific tracker engineering rather than a general constant-context mechanism.
  2. [Results / Experiments] The abstract and results sections report concrete success rates and token reductions but supply no details on baselines, variance across seeds, data splits, or exact training procedures (hyper-parameters, number of RL steps, reward shaping). This absence prevents verification that the numbers support the stated claims.
minor comments (1)
  1. [Results] The token-reduction claim (2–7×) would be clearer if accompanied by per-environment tables showing exact token counts for the ReAct baselines versus the proposed method.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive review and for identifying areas where additional evidence and transparency would strengthen the manuscript. We respond to each major comment below and describe the revisions we will incorporate.

read point-by-point responses

  1. Referee: The central claim that the deterministic tracker’s compact state block is informationally complete for unseen tasks is load-bearing, yet no ablations are described that remove or corrupt individual elements (subgoal progress flags, observation summaries, etc.) and measure the resulting drop in success rate or change in failure modes on held-out tasks. Without such evidence it remains possible that the 89.6% ALFWorld figure relies on environment-specific tracker engineering rather than a general constant-context mechanism.

    Authors: We appreciate the referee highlighting the need to directly validate informational completeness. The tracker is deterministic by construction and encodes only task progress and subgoal information that is required for the current step, with no environment-specific heuristics beyond the task definition itself. The fact that the same framework yields strong unseen-task performance across three dissimilar environments (ALFWorld, WebShop, SciWorld) already indicates that the state block is not narrowly engineered. Nevertheless, we agree that targeted ablations would provide clearer evidence. In the revised manuscript we will add a dedicated ablation study that removes or perturbs individual state-block fields (subgoal flags, observation summaries, etc.) and reports the resulting success-rate drops and failure-mode shifts on held-out tasks. This will isolate the contribution of each element and further support the generality of the constant-context approach. revision: yes

  2. Referee: The abstract and results sections report concrete success rates and token reductions but supply no details on baselines, variance across seeds, data splits, or exact training procedures (hyper-parameters, number of RL steps, reward shaping). This absence prevents verification that the numbers support the stated claims.

    Authors: We agree that reproducibility requires explicit experimental details. While the manuscript already identifies the baselines as controlled ReAct prompting and describes the overall training pipeline (SFT followed by online RL with tracker-supplied rewards), we acknowledge that variance, split definitions, and hyper-parameter values are not reported at the level needed for verification. In the revision we will expand the Experiments and Results sections with: (i) success rates reported as mean ± standard deviation over multiple random seeds, (ii) precise descriptions of the seen/unseen data splits used for each environment, and (iii) a table listing all hyper-parameters, the number of RL steps, batch sizes, and the exact reward-shaping functions employed by the deterministic tracker. These additions will enable readers to fully reproduce and assess the reported success rates and token reductions. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical training recipe with no derivations or self-referential reductions.

full rationale

The paper describes an empirical method for constant-context skill learning via task-family modules, deterministic trackers for state blocks, SFT, and RL. No equations, closed-form derivations, or load-bearing self-citations are present that reduce predictions to fitted inputs or prior author results by construction. Results are benchmarked externally on ALFWorld, WebShop, and SciWorld against published baselines, satisfying the self-contained criterion for score 0.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available, so free parameters, axioms, and invented entities cannot be exhaustively audited; the framework implicitly assumes recurring workflows exist and that a compact state suffices.

pith-pipeline@v0.9.0 · 5567 in / 1110 out tokens · 59703 ms · 2026-05-08T16:45:43.733804+00:00 · methodology

discussion (0)

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Reference graph

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