User: 

You are given access to make searches on the Internet. The format for making a search is:
1. After the user sends a message, you will decide whether to make a search. If you choose to make a search, you will output a single message, starting with the word <web_search>, followed by ONLY your web search query. After your search query, you must output the token </web_search> to denote the end of the search query. Keep your search query very concise.
2. The system will then append the web search results at the end of the user message, starting with the token <web_search_results>. Take note that the user CANNOT SEE the content after <web_search_results> - YOU should use these results appropriately to improve your response. Additionally, If the web search results are already present in the user's message, then you MUST NOT request another web search.

IMPORTANT! You should choose to search the web ONLY if ANY of the following circumstances are met:
1. User is asking about current events or something that requires real-time information (news, sports scores, 'latest' information, etc.)
2. User is asking about some term you are unfamiliar with (e.g. if it's a little-known term, if it's a person you don't know well, etc.)
3. User is asking about anything that involves numerical facts (e.g. distances between places, population count, sizes, date and time, etc.)
4. User explicitly asks you to search or provide links to references.
If the user's query does NOT require a web search, YOU MUST NEVER run one for no reason. However, you must not refuse to search the web if the user asks you to do so.
If you are highly unsure about whether to make a search (e.g. if the user's query is vague or if you think you are unfamiliar with the query to a certain extent), you may directly ask the user for clarification.

When using web search results, you are encouraged to cite references where appropriate, using inline links in standard markdown format.
You are also allowed to make use of web search results from previous messages, if they are STRONGLY RELEVANT to the current message.

When you are not running a web search, respond completely as per normal - there is NO NEED to mention that you're not searching. 
Remember that you MUST NOT make up any false information.
Assistant: Understood. I will strictly follow these instructions in this conversation.
User: descript grpo in llm rl training
Assistant:  ItIt looks like you're asking about **"GRPO"** in the context of **large language model (LLM) reinforcement learning (RL) training**. However, the term "GRPO" doesn't directly correspond to a widely recognized algorithm or concept in LLM or RL literature as of now.

Could you please clarify or verify the term? Here are a few possibilities you might be referring to:

1. **"GPO" or "GRPO" as a typo or abbreviation**:  
   - You might mean **PPO** (Proximal Policy Optimization), a very popular RL algorithm often used in fine-tuning LLMs like ChatGPT using RL from human feedback (RLHF).
   - Or you might refer to some variant or new method related to **policy optimization** methods.

2. **If GRPO refers to a specific paper or method:**  
   - Please provide a little more context or a full name/expansion of the acronym.

---

### General description of relevant concepts in LLM RL training (assuming PPO or policy optimization)

**Reinforcement learning fine-tuning of LLMs**, such as RLHF, often involves:

- **Policy**: The language model itself, which outputs tokens/actions.
- **Reward signal**: A reward model assesses outputs (e.g., from human feedback or heuristics).
- **Policy optimization algorithm**: Methods like PPO adjust the model parameters to maximize expected reward while keeping updates stable.

**Proximal Policy Optimization (PPO)** is a common technique here:

- PPO optimizes a surrogate objective that prevents large policy updates by clipping the probability ratio between new and old policies.
- This helps training stability since large policy changes can degrade the learned behavior.
- In RLHF, PPO is used to fine-tune the LLM to better align with human preferences.

---

If you provide more details, I can give a specific explanation of **GRPO** or the relevant term you’re referring to!
User: grpo is using in deepseek-r1

<web_search_results> for "Deepseek-R1 GRPO reinforcement learning training":

{
  "results": {
    "organic": [
      {
        "title": "Understanding GRPO Optimization in DeepSeek R1 - Medium",
        "link": "https://medium.com/@bingqian/understanding-grpo-optimization-in-deepseek-r1-a-step-by-step-breakdown-with-examples-c0f68fd97823",
        "snippet": "The DeepSeek R1 model has demonstrated remarkable success in reinforcement learning, largely due to its innovative optimization techniques.",
        "date": "Mar 20, 2025",
        "position": 1
      },
      {
        "title": "DeepSeek-R1 Dissection: Understanding PPO & GRPO Without Any ...",
        "link": "https://huggingface.co/blog/NormalUhr/grpo",
        "snippet": "Let's walk step by step through why final scores alone are insufficient, how Critic, Clip, and Reference Model come into play, and finally how GRPO extends ...",
        "date": "Feb 7, 2025",
        "position": 2
      },
      {
        "title": "Bite: How Deepseek R1 was trained - Philschmid",
        "link": "https://www.philschmid.de/deepseek-r1",
        "snippet": "Group Relative Policy Optimization (GRPO) is a reinforcement learning algorithm to improve the reasoning capabilities of LLMs. It was introduced ...",
        "date": "Jan 17, 2025",
        "position": 4
      },
      {
        "title": "Understanding Reinforcement Learning in DeepSeek-R1 - Medium",
        "link": "https://medium.com/@la_boukouffallah/understanding-reinforcement-learning-in-deepseek-r1-079d3360ca6c",
        "snippet": "Instead, GRPO estimates baseline rewards from grouped outputs, reducing computational demands while maintaining robust training signals. GRPO: A ...",
        "date": "Feb 3, 2025",
        "position": 5
      },
      {
        "title": "DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via ...",
        "link": "https://arxiv.org/abs/2501.12948",
        "snippet": "DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrates ...",
        "date": "Jan 22, 2025",
        "position": 6
      },
      {
        "title": "mini-deepseek-r1-aha-grpo.ipynb - GitHub",
        "link": "https://github.com/philschmid/deep-learning-pytorch-huggingface/blob/main/training/mini-deepseek-r1-aha-grpo.ipynb",
        "snippet": "In this blog post we want to recreate the small \"aha moment\" of DeepSeek-R1 using Group Relative Policy Optimization (GRPO) and the Countdown Game.",
        "position": 7
      },
      {
        "title": "DeepSeek-R1 — Intuitively and Exhaustively Explained",
        "link": "https://iaee.substack.com/p/deepseek-r1-intuitively-and-exhaustively",
        "snippet": "To deal with these issues, The DeepSeek team created a reinforcement learning algorithm called “Group Relative Policy Optimization (GRPO)”. Why ...",
        "date": "Feb 3, 2025",
        "position": 8
      },
      {
        "title": "GRPO Reinforcement Learning Explained (DeepSeekMath Paper)",
        "link": "https://aipapersacademy.com/deepseekmath-grpo/",
        "snippet": "It is worth mentioning that in DeepSeek-R1, instead of a reward model, the reward was calculated based on rules, such as whether a math answer ...",
        "date": "Apr 14, 2025",
        "position": 9
      },
      {
        "title": "Deep Dive into DeepSeek R1: Revolutionizing LLM Reinforcement ...",
        "link": "https://www.linkedin.com/pulse/deepseek-r1-reinforcement-learning-llm-group-relative-mitul-tiwari-c8gmf",
        "snippet": "In this blog post I will cover a new reinforcement learning technique developed by DeepSeek called Group Relative Policy Optimization (GRPO).",
        "date": "Jan 28, 2025",
        "position": 10
      }
    ]
  }
}
Assistant: