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
User: do you know its loss

<web_search_results> for "GRPO loss function DeepSeek-R1":

{
  "results": {
    "organic": [
      {
        "title": "Understanding the Math Behind GRPO — DeepSeek-R1-Zero",
        "link": "https://medium.com/yugen-ai-technology-blog/understanding-the-math-behind-grpo-deepseek-r1-zero-9fb15e103a0a",
        "snippet": "GRPO Objective Function · The surrogate loss function in GRPO. Uses both clipping and KL Divergence.",
        "date": "Feb 6, 2025",
        "position": 1
      },
      {
        "title": "The Math Behind DeepSeek: A Deep Dive into Group Relative ...",
        "link": "https://medium.com/@sahin.samia/the-math-behind-deepseek-a-deep-dive-into-group-relative-policy-optimization-grpo-8a75007491ba",
        "snippet": "This blog dives into the math behind Group Relative Policy Optimization (GRPO), the core reinforcement learning algorithm that drives DeepSeek's exceptional ...",
        "date": "Jan 26, 2025",
        "position": 2
      },
      {
        "title": "Bite: How Deepseek R1 was trained - Philschmid",
        "link": "https://www.philschmid.de/deepseek-r1",
        "snippet": "KL Divergence: GRPO directly incorporates the KL divergence term into the loss function, while PPO often uses it as part of the reward signal.",
        "date": "Jan 17, 2025",
        "position": 3
      },
      {
        "title": "DeepSeek R1: Understanding GRPO and Multi-Stage Training",
        "link": "https://ai.plainenglish.io/deepseek-r1-understanding-grpo-and-multi-stage-training-5e0bbc28a281",
        "snippet": "The KL divergence term is incorporated directly into the loss function, ensuring the model balances exploration and stability. Deepdive into ...",
        "date": "Jan 27, 2025",
        "position": 4
      },
      {
        "title": "How DeepSeek R1, GRPO, and Previous DeepSeek Models Work",
        "link": "https://ghost.oxen.ai/how-deepseek-r1-grpo-and-previous-deepseek-models-work/",
        "snippet": "They reformulate the loss function as a classification task between prompt completions and show that they can get competitive performance ...",
        "date": "Feb 4, 2025",
        "position": 5
      },
      {
        "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": 6
      },
      {
        "title": "DeepSeek-R1 — Intuitively and Exhaustively Explained",
        "link": "https://iaee.substack.com/p/deepseek-r1-intuitively-and-exhaustively",
        "snippet": "In this article we'll discuss DeepSeek-R1, the first open-source model that exhibits comparable performance to closed source LLMs.",
        "date": "Feb 3, 2025",
        "position": 7
      },
      {
        "title": "policy-gradient/GRPO-Zero: Implementing DeepSeek R1's ... - GitHub",
        "link": "https://github.com/policy-gradient/GRPO-Zero",
        "snippet": "Group Relative Policy Optimization (GRPO) is an algorithm proposed by Deepseek for training large language models with reinforcement learning. The idea is ...",
        "sitelinks": [
          {
            "title": "Grpo:Zero",
            "link": "https://github.com/policy-gradient/GRPO-Zero#:~:text=GRPO%3AZero"
          },
          {
            "title": "Algorithm",
            "link": "https://github.com/policy-gradient/GRPO-Zero#:~:text=Algorithm,-Group%20Relative%20Policy%20Optimization%20%28"
          },
          {
            "title": "Reward Function",
            "link": "https://github.com/policy-gradient/GRPO-Zero#:~:text=Reward%20Function"
          }
        ],
        "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: The **GRPO (Group Relative Policy Optimization)** loss used in DeepSeek-R1 is a reinforcement learning objective that extends ideas from PPO by incorporating both clipping and KL divergence terms directly into the surrogate loss function.

Key points about the GRPO loss function are:

- It reformulates the policy optimization loss as a classification-like objective between prompt completions.
- The KL divergence between the new policy and the reference policy is integrated directly into the loss to control how much the policy can change, helping maintain stability.
- A clipping mechanism similar to PPO is also used to prevent large updates that could destabilize training.

In essence, the GRPO loss looks like a combination of:

- A clipped surrogate objective ensuring policy updates are not too big.
- A KL divergence penalty embedded in the loss to keep the updated policy close to the reference policy.

This differs from PPO, where KL divergence may be used more as a separate penalty or in the reward signal rather than explicitly in the loss.

If you'd like, I can provide some pseudocode or mathematical formulation of the GRPO loss based on these details!
User: defined in latex
Assistant: