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  "path": "/t/a-simple-idea-separating-a-thinker-and-observer-model-to-detect-reasoning-loops/174134#post_6",
  "publishedAt": "2026-03-11T04:02:24.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "the Agents Course",
    "Hugging Face’s `smolagents` library",
    "Stanford Encyclopedia of Philosophy",
    "arXiv",
    "OpenAI",
    "ACL Anthology",
    "Anthropic",
    "Hugging Face",
    "DeepLearning.AI - Learning Platform"
  ],
  "textContent": "If for beginners, the Agents Course covering Hugging Face’s `smolagents` library and other agentic RAG frameworks includes orchestration-related content, which might be helpful.\n\n* * *\n\nA good way to explore this topic as a beginner is to treat it as **three connected layers** :\n\n  1. **logic background** — why self-reference and meta-level description matter,\n  2. **AI architecture** — how modern systems separate generating from checking,\n  3. **small experiments** — how to test whether an “Observer” actually helps. (Stanford Encyclopedia of Philosophy)\n\n\n\n## 1. Start with the logic background\n\nYour idea becomes much clearer if you first learn the difference between an **object language** and a **metalanguage**. The Stanford Encyclopedia’s entries on **Tarski’s truth definitions** and the **Liar Paradox** are the best starting points. They explain why liar-style sentences push logicians toward a hierarchy: one level makes statements, and another level talks about the truth of those statements. That is the clean philosophical ancestor of your “Thinker vs Observer” intuition. (Stanford Encyclopedia of Philosophy)\n\nAfter that, a useful optional next step is the **Revision Theory of Truth**. It is not necessary at first, but it is helpful if you become interested in how self-referential truth can be modeled dynamically rather than eliminated by hierarchy alone. That gives you a richer picture of why “looping” is not just a software bug; sometimes it reflects a deep structural problem in the semantics. (Stanford Encyclopedia of Philosophy)\n\n## 2. Then read the AI papers that are closest to your idea\n\nThe first paper I would read is **Training Verifiers to Solve Math Word Problems**. It is one of the clearest examples of a “Thinker/Observer” split in modern ML: one model generates candidate solutions, and a verifier ranks them. The paper also introduced GSM8K, which later became a major reasoning benchmark. (arXiv)\n\nNext, read **Let’s Verify Step by Step**. This is especially important for your case because it moves from checking only the final answer to checking the **intermediate reasoning steps**. That is much closer to your concern about contradiction, circularity, and loops inside the reasoning process itself. The paper reports strong gains and released PRM800K, a large step-level feedback dataset. (OpenAI)\n\nThen read **LLM Critics Help Catch LLM Bugs**. This paper is valuable because it shows a different version of the Observer role: not just scoring answers, but writing critiques that help humans or systems spot mistakes more accurately. That gives you a concrete picture of what a specialized Observer can look like in practice. (OpenAI)\n\nAfter that, read **Reflexion** and **Self-Refine**. These are easier to understand than some formal verifier papers because the loop is very intuitive: generate, get feedback, revise. Reflexion is especially relevant when external feedback exists, and Self-Refine is useful for seeing both the promise and the limits of self-feedback. (arXiv)\n\nA good final paper in this stage is **Language Agent Tree Search (LATS)**. It is less about paradox and more about how reasoning, acting, planning, and feedback can be combined in a structured search process. It helps you see that “Observer” does not always have to be a passive checker; sometimes it is embedded inside a broader control/search loop. (arXiv)\n\n## 3. Read one “warning” paper early\n\nDo not wait too long before reading **Large Language Models Cannot Self-Correct Reasoning Yet**. It is important because it stops you from overestimating what reflection can do. Its main message is that a model often struggles to fix its own reasoning without reliable external feedback, and sometimes self-correction even makes performance worse. That is one of the strongest reasons your Observer should ideally have a different objective, stronger supervision, or better tools than the Thinker. (arXiv)\n\nIf you want one survey after that, **When Can LLMs Actually Correct Their Own Mistakes?** is a good bridge. It reviews when self-correction works, when it does not, and why external feedback matters so much. (ACL Anthology)\n\n## 4. Read one practical guide so the theory stays grounded\n\nFor practical engineering judgment, I would read **Building Effective AI Agents** from Anthropic early rather than late. It argues that the most successful systems are often built from simple, composable patterns rather than overly complicated agent societies. That is a very good lesson for your case: your first prototype should be small and measurable, not grand. (Anthropic)\n\n## 5. Use a beginner-friendly course alongside the papers\n\nThe **Hugging Face AI Agents Course** is a good companion because it starts from basics and walks through the thought–action–observation cycle. Its sections on **observations** , agent structure, and the bonus unit on **observability and evaluation** are especially relevant to your idea, because they move you from “interesting concept” to “how do I inspect an agent’s behavior step by step?” (Hugging Face)\n\nThe newer **Agentic AI** course from DeepLearning.AI is also useful because it emphasizes disciplined development, evals, and error analysis rather than just building flashy workflows. That is exactly the mindset you want for an Observer-style project. (DeepLearning.AI - Learning Platform)\n\n## 6. The best beginner experiments\n\nThe first experiment I would run is **contradiction detection on synthetic reasoning traces**. Create short chains of reasoning, some consistent and some with one hidden contradiction. The Thinker produces or paraphrases the chain, and the Observer must identify whether a contradiction exists and, if possible, the first conflicting pair of steps. This is simple, measurable, and directly connected to the process-supervision and verifier literature. (arXiv)\n\nThe second experiment is **circular-support detection**. Write examples where the conclusion is smuggled in as a premise, or where statement A supports B and B supports A. Then compare three setups: no observer, same-model self-review, and separate observer. That experiment gets at the heart of your idea and also directly tests the warning from the self-correction papers. (arXiv)\n\nThe third experiment is **loop detection in a toy agent**. Give a simple agent a small task with tools, such as searching a tiny database or navigating a mini environment, and intentionally create cases where it repeats the same failed action. The Observer’s job is to say “no progress,” and the Controller’s job is to stop, retry with a new plan, or abstain. This is a concrete way to turn your idea into something operational. The course material on observation/evaluation and the practical agent guides are well aligned with this kind of setup. (Hugging Face)\n\nThe fourth experiment is **final-answer judging vs step-level judging**. Take the same tasks and compare an Observer that only sees the final answer with an Observer that sees each reasoning step. This directly tests the intuition behind process supervision: sometimes the final answer hides where the reasoning went wrong, while step-level inspection can reveal it. (OpenAI)\n\n## 7. A very good beginner research question\n\nIf you want one clean question to guide your reading and experiments, I would use this:\n\n> **When does a separate Observer improve reasoning more than simple self-correction?**\n\nThat question is narrow enough to test and broad enough to connect logic, verifiers, critics, and monitoring. It also naturally leads to comparisons that matter:\n\n  * same model vs separate model,\n  * final-answer check vs step-level check,\n  * no external signal vs external signal,\n  * revise vs abstain. (arXiv)\n\n\n\n## 8. A practical progression for reading\n\nA good order is:\n\n  1. **Tarski’s Truth Definitions** and **Liar Paradox** for the foundation. (Stanford Encyclopedia of Philosophy)\n  2. **Training Verifiers to Solve Math Word Problems** for the clearest ML analogue. (arXiv)\n  3. **Let’s Verify Step by Step** for process-level oversight. (OpenAI)\n  4. **LLM Critics Help Catch LLM Bugs** for the critic role. (OpenAI)\n  5. **Reflexion** and **Self-Refine** for intuitive iterative-feedback systems. (arXiv)\n  6. **Large Language Models Cannot Self-Correct Reasoning Yet** as the main caution. (arXiv)\n  7. **Building Effective AI Agents** and the agent courses for practical implementation. (Anthropic)\n\n\n\n## 9. What to pay attention to while reading\n\nWhile reading, I would keep four questions in front of you:\n\n  * **What exactly is being checked?** final answer, individual steps, or full trace?\n  * **What gives the checker an advantage?** different training, external tools, more candidates, or narrower scope?\n  * **What happens after an error is found?** revise, retry, switch strategy, or abstain?\n  * **How is success measured?** accuracy, fewer loops, better critiques, safer behavior, or more appropriate abstention? (arXiv)\n\n\n\nThose four questions will stop the topic from becoming vague.\n\n## 10. The most important beginner pitfall\n\nThe biggest beginner mistake here is to think that “an Observer” is automatically enough. The literature points the other way: monitoring and critique are useful, but they work best when the system is designed so that the observer has a real advantage or real evidence. Otherwise, you often get elegant-sounding reflection that does not reliably improve reasoning. (arXiv)\n\nA second pitfall is to aim too high too early. The Liar Paradox is a good motivation, but it is not a good first benchmark. Start with contradiction, circular support, and repeated-state detection. Those are much easier to define and measure. The monitorability work is also a reminder that “can we observe the reasoning?” is itself a serious empirical question, not something to assume for free. (OpenAI)\n\n## 11. A simple first-month plan\n\nIn the first week, read the two SEP entries and one beginner course unit on agent structure/observations. In the second week, read the verifier paper and the process-supervision paper. In the third week, build one toy contradiction or loop-detection experiment. In the fourth week, compare three variants: no observer, same-model reviewer, and separate observer. That would already give you a much deeper understanding than reading passively for a month. (Stanford Encyclopedia of Philosophy)\n\n## Bottom line\n\nIf you want the shortest recommendation set, I would start with these seven:\n\n  * **Tarski’s Truth Definitions**\n  * **Liar Paradox**\n  * **Training Verifiers to Solve Math Word Problems**\n  * **Let’s Verify Step by Step**\n  * **LLM Critics Help Catch LLM Bugs**\n  * **Large Language Models Cannot Self-Correct Reasoning Yet**\n  * **Hugging Face AI Agents Course** or **Anthropic’s Building Effective AI Agents** (Stanford Encyclopedia of Philosophy)\n\n\n\nThat set gives you the logic foundation, the main modern architectures, the key caution, and a practical path into experimentation.",
  "title": "A simple idea: separating a \"Thinker\" and \"Observer\" model to detect reasoning loops"
}