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Model: yuanqianhao/MemSearcher-3B Source: Original Platform
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---
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license: apache-2.0
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base_model:
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- Qwen/Qwen2.5-3B-Instruct
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- reinforcement-learning
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- search-agent
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- rag
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- memory
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- grpo
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---
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# MemSearcher-3B
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<p align="center">
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📄 <a href="https://arxiv.org/abs/2511.02805">Paper</a> |
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💻 <a href="https://github.com/icip-cas/MemSearcher">Code</a>
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</p>
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## Model Summary
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**MemSearcher-3B** is a search agent built on [`Qwen/Qwen2.5-3B-Instruct`]( https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) and trained with
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**multi-context GRPO**, the end-to-end reinforcement-learning method introduced in
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[MemSearcher](https://arxiv.org/abs/2511.02805).
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Unlike ReAct-style agents that concatenate the *entire* interaction history into the context — producing long,
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noisy inputs and linearly growing compute — MemSearcher maintains a **compact, iteratively-updated memory**.
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At every turn the backbone LLM sees only two short inputs, the **question** and the current **memory**, then it
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reasons, decides whether to search, and rewrites the memory to keep only question-relevant information. This keeps
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the context length **nearly constant across turns** without sacrificing accuracy.
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## Evaluation
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Exact Match (EM) on seven single- and multi-hop QA benchmarks (Wikipedia-18 corpus):
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| Model | NQ | TriviaQA | PopQA | HotpotQA | 2Wiki | MuSiQue | Bamboogle | Avg. |
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|---|---|---|---|---|---|---|---|---|
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| **MemSearcher 3B** | 47.0 | 63.8 | 47.9 | 43.9 | 43.5 | 17.9 | 42.4 | **43.8** |
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| MemSearcher 7B | 52.7 | 68.1 | 47.8 | 50.8 | 48.6 | 25.8 | 48.8 | 48.9 |
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| MemSearcher 14B | 53.7 | 71.1 | 48.8 | 51.8 | 51.5 | 27.2 | 57.6 | 51.7 |
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This card is for the **3B** model (highlighted row).
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## Key Features
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- 🧠 **Compact memory** — keeps only question-relevant information instead of the full history.
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- 📏 **Near-constant context** — token count stays stable across multi-turn interactions, cutting compute/memory.
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- 🎯 **Multi-context GRPO** — propagates trajectory-level advantages to every turn for true end-to-end RL.
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- 🔍 **Strong QA performance** — outperforms strong history-concatenation (ReAct-style) baselines across 7 benchmarks.
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## How It Works
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At turn *i* the context is just `(question, memory_{i-1})`. The model:
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1. **Reasons** inside `<think>...</think>`;
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2. **Searches** when it lacks knowledge (a `wikipedia_search` tool call), or answers in `\boxed{}` when the memory suffices;
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3. **Updates the memory** from the retrieved results, retaining only what helps answer the question.
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Training uses **multi-context GRPO**: it samples a group of trajectories, then propagates each trajectory's
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advantage to all of its per-turn contexts, enabling stable end-to-end optimization of reasoning, search, and
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memory management.
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## Quickstart
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MemSearcher is an **agent** that needs a Wikipedia retriever and the multi-turn orchestration loop. The simplest
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path is the released code:
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```bash
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git clone https://github.com/icip-cas/MemSearcher && cd MemSearcher
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# 1) start the wiki-18 retriever (see README "Retriever Serving")
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# 2) serve this model with SGLang
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MODEL_PATH=yuanqianhao/MemSearcher-3B TP=2 PORT=80 bash launch_server.sh
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# 3) run the agentic evaluation
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GENERATOR_MODEL=yuanqianhao/MemSearcher-3B DATA_DIR=./data SAVE_DIR=./eval \
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SGL_REMOTE_URL=http://127.0.0.1:80 RETRIEVER_URL=http://127.0.0.1:8000/search bash eval.sh
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```
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The model weights load with standard `transformers` (`AutoModelForCausalLM.from_pretrained("yuanqianhao/MemSearcher-3B")`),
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but reproducing the reported numbers requires the retriever + agent loop above.
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## Training Data
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Trained on the **NQ + HotpotQA** training split — the same data as
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[Search-R1](https://github.com/PeterGriffinJin/Search-R1) — re-wrapped into MemSearcher's re_call format.
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See the code repository for the exact preparation script.
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## Citation
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```bibtex
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@article{yuan2025memsearcher,
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title={MemSearcher: Training LLMs to Reason, Search and Manage Memory via End-to-End Reinforcement Learning},
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author={Yuan, Qianhao and Lou, Jie and Li, Zichao and Chen, Jiawei and Lu, Yaojie and Lin, Hongyu and Sun, Le and Zhang, Debing and Han, Xianpei},
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journal={arXiv preprint arXiv:2511.02805},
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year={2025}
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}
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```
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## License
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Released under Apache-2.0, following the base model [`Qwen/Qwen2.5-3B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct).
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"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user