Learning Stateful Predictive Knowledge From Experience
Quick Answer
The paper introduces Stateful Knowledge Learning (SKL), which enhances large language models' predictive capabilities by focusing on state-grounded knowledge rather than trajectory-level reflection.
Quick Take
SKL employs self-distillation and reinforcement learning algorithms to autonomously extract and utilize predictive knowledge, outperforming traditional reflection-based training in interactive environments and complex reasoning tasks.
Key Points
- SKL shifts focus from trajectory-level insights to explicit, state-grounded predictive assessments.
- Introduces two algorithms: SKL-SD (self-distillation) and SKL-RL (reinforcement learning).
- Demonstrated superior performance in environments like WebShop and ScienceWorld.
- Enables knowledge bootstrapping and enhances generalization in predictive tasks.
- Significantly outperforms existing reflection-based training paradigms.
DeepSignal Analysis
What happened
The paper presents Stateful Knowledge Learning (SKL), a method that enhances large language models' predictive abilities by focusing on state-grounded knowledge. SKL employs self-distillation and reinforcement learning to autonomously extract predictive knowledge, showing improved performance over traditional methods in complex reasoning tasks and interactive environments.
Key evidence
- SKL shifts the focus from trajectory-level summarization to maintaining explicit, state-grounded predictive assessments.
- The paper introduces two algorithms, SKL-SD and SKL-RL, which train agents to autonomously extract and leverage state-grounded predictive knowledge.
- Experiments conducted on environments like WebShop and ScienceWorld demonstrate that SKL significantly outperforms reflection-based training paradigms.
Why it matters
This research addresses limitations in existing training methods for large language models, which often rely on past experiences without predictive foresight. By introducing SKL, the authors suggest a more robust framework for learning that could lead to better generalization and decision-making in complex tasks. This could have implications for the development of more effective AI systems in various applications.
Paper Resources
📖 Reader Mode
~2 min readAbstract:As large language model (LLM) agents increasingly learn from experience, they primarily rely on trajectory-level reflection to extract insights. Viewed through the lens of predictive knowledge, we argue that this approach operates on episodic hindsight rather than predictive foresight, yielding brittle, path-dependent heuristics. To address this, we propose Stateful Knowledge Learning (SKL). SKL shifts the agent's focus from trajectory-level summarization to maintaining Stateful Knowledge: explicit, declarative predictive assessments anchored to state. We first demonstrate a motivating example showing how stateful knowledge provides granularity, enhances generalization, and enables knowledge bootstrapping. To further scale up the idea, we introduce two algorithms via self-distillation (SKL-SD) and reinforcement learning (SKL-RL), training agents to autonomously extract state-grounded predictive knowledge from experience and learn to leverage it for policy making. Experiments on interactive environments (WebShop, ScienceWorld) and a complex reasoning task (ChessPuzzles) demonstrate that equipping models with the inherent ability to learn stateful predictive knowledge significantly outpaces current reflection-based training paradigms.
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.28638 [cs.CL] |
| (or arXiv:2607.28638v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.28638 arXiv-issued DOI via DataCite |
Submission history
From: Yan Song [view email]
[v1]
Tue, 19 May 2026 17:09:32 UTC (962 KB)
— Originally published at arxiv.org
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