⚠️ A tool-using agent, not a standalone chatbot

The model emits <tool_call> shell commands that must be executed against the Wikipedia corpus and fed back as <tool_response> turns. To use it you need: (1) the corpus PeterJinGo/wiki-18-corpus, (2) a tool-calling vLLM server, and (3) the GrepSeek inference harness (grep tool

• agent loop), all in the code repo.

Usage

bash
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git clone https://github.com/alirezasalemi7/grepseek && cd grepseek
# env: TRAINING_ENV.md  ·  corpus: cold_start_sft/download_corpus.py

# 1. serve this checkpoint
MODEL_PATH=alireza7/GrepSeek-Qwen3.5-9B-SFT bash rl/serve_rl.sh         # -> http://localhost:10730/v1

# 2. run the agent (paper inference: temperature 0.6, <=6 turns, 16k context)
GREPSEEK_CORPUS_ROOT=/path/to/wiki_18_corpus \
  bash inference/run_inference.sh --base_url http://localhost:10730/v1 \
    --model grepseek --temperature 0.6 --input my_questions.jsonl --out_dir out

Evaluation (token-F1 / EM, micro-average over 7 QA benchmarks)

This SFT-only policy already substantially beats the untuned base model, but RL adds large gains on multi-hop reasoning:

(7 benchmarks: NQ, TriviaQA, PopQA, HotpotQA, 2WikiMultiHopQA, MuSiQue, Bamboogle; trained only on NQ + HotpotQA, the rest are out-of-distribution.)

License

Inherits the license of the base model Qwen/Qwen3.5-9B — confirm and update the license field above if needed.

Citation

bibtex
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@misc{salemi2026grepseektrainingsearchagents,
      title={GrepSeek: Training Search Agents for Direct Corpus Interaction},
      author={Alireza Salemi and Chang Zeng and Atharva Nijasure and Jui-Hui Chung and Razieh Rahimi and Fernando Diaz and Hamed Zamani},
      year={2026},
      eprint={2605.29307},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2605.29307},
}