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+ add example list doc from the dataset perspective
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‎docs/sphinx_doc/source/index.rst‎

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tutorial/example_dpo.md
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tutorial/example_megatron.md
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tutorial/example_data_functionalities.md
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tutorial/example_dataset_perspective.md
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‎docs/sphinx_doc/source/tutorial/example_dataset_perspective.md‎

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# Example List from the Dataset Perspective
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This guide provide an example list from the dataset perspective, where you can find out what datasets the examples have covered easily.
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| Dataset | Algorithm | Use Case | References |
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| --- | --- | --- | --- |
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| [openai/gsm8k](https://huggingface.co/datasets/openai/gsm8k) | GRPO | Regular RFT | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k), [doc](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_reasoning_basic.html) |
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| | GRPO | Asynchronous training | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/async_gsm8k), [doc](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_async_mode.html#) |
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| | Multi-Step GRPO | AgentScope ReAct agent training | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/agentscope_react), [doc](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_react.html) |
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| | AsymRE | Regular RFT | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/asymre_gsm8k) |
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| | CISPO | Regular RFT | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/cispo_gsm8k) |
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| | GRPO | Training with prioritized tasks | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_task_pipeline), [doc](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_task_pipeline) |
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| | GRPO | Training with reward reshaping on experiences | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_experience_pipeline), [doc](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_data_functionalities.html#example-data-processor-for-experience-pipeline) |
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| | GRPO | Training with RULER (Relative Universal LLM-Elicited Rewards) | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_ruler) |
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| | GRPO | Training a policy model as its own reward model | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_trainable_ruler) |
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| | GRPO | Training using LoRA | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_lora_gsm8k) |
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| | OPMD | Off-policy RFT | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/opmd_gsm8k), [doc](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_reasoning_advanced.html) |
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| | REC | Training with group-relative reinforce variants | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/rec_gsm8k) |
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| | sPPO | Training with sPPO algorithm | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/sppo_gsm8k) |
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| | TOPR | Tapered off-policy RFT | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/topr_gsm8k) |
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| math | GRPO | Training with rewards from RM-Gallery | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_math) |
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| | AsymRE | Regular RFT | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/asymre_math) |
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| | MIX | Training with "expert" data generated by a more advanced LLM | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/mix_math), [doc](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_mix_algo.html) |
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| [open-r1/DAPO-Math-17k-Processed](https://huggingface.co/datasets/open-r1/DAPO-Math-17k-Processed) | GRPO | Regular RFT | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/dapo_math) |
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| [LLM360/guru-RL-92k](https://huggingface.co/datasets/LLM360/guru-RL-92k) | GRPO | Training with bayesian online task selection | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/bots) |
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| [callanwu/WebWalkerQA](https://huggingface.co/datasets/callanwu/WebWalkerQA) | Multi-Step GRPO | Multi-turn web search agent training | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/agentscope_websearch) |
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| [ALFWorld](https://github.com/alfworld/alfworld) | GRPO | Concatenated multi-turn RFT | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_alfworld), [doc](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_multi_turn.html) |
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| | Multi-Step GRPO | General multi-step RFT | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_alfworld_general_multi_step), [doc](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_step_wise.html#) |
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| [corbt/enron-emails](https://huggingface.co/datasets/corbt/enron-emails) | Multi-Step GRPO | Multi-turn email search agent training | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_email_search), [doc](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_search_email.html) |
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| [Frozen Lake](https://gymnasium.farama.org/environments/toy_text/frozen_lake/) | GRPO | Concatenated multi-turn RFT | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_frozen_lake) |
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| [anisha2102/RaR-Medicine](https://huggingface.co/datasets/anisha2102/RaR-Medicine) | GRPO | Training with rewards from LLM judge and rubrics for a non-verifiable medicine QA task | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_rubric_as_reward) |
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| [SciWorld](https://github.com/allenai/ScienceWorld) | GRPO | Regular RFT | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_sciworld) |
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| [Team-ACE/ToolACE](https://huggingface.co/datasets/Team-ACE/ToolACE) | GRPO | Regular RFT for tool calling | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_toolcall) |
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| [hiyouga/geometry3k](https://huggingface.co/datasets/hiyouga/geometry3k) | GRPO | Regular RFT for VLM | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_vlm) |
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| | MIX | Training with "expert" data generated by a more advanced LLM | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/mix_vlm) |
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| [WebShop](https://github.com/princeton-nlp/WebShop) | GRPO | Concatenated multi-turn RFT | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_webshop), [doc](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_multi_turn.html) |
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| [datajuicer/RealMedConv](https://huggingface.co/datasets/datajuicer/RealMedConv) | GRPO | Regular RFT for learning to ask in a proactive way | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/learn_to_ask) |
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| [datajuicer/Trinity-ToolAce-RL-split](https://huggingface.co/datasets/datajuicer/Trinity-ToolAce-RL-split) | CHORD | Training with dynamic SFT + RL integration | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/mix_chord) |
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| [datajuicer/Trinity-ToolAce-SFT-split](https://huggingface.co/datasets/datajuicer/Trinity-ToolAce-SFT-split) | CHORD | Training with dynamic SFT + RL integration | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/mix_chord) |
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| [Jiayi-Pan/Countdown-Tasks-3to4](https://huggingface.co/datasets/Jiayi-Pan/Countdown-Tasks-3to4) | PPO | Training based on the critic model | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/ppo_countdown) |
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| | PPO | Training with Megatron-LM as the backend. | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/ppo_countdown_megatron) |
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| | PPO | Training with experience replay | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/ppo_countdown_exp_replay) |
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| [open-r1/Mixture-of-Thoughts](https://huggingface.co/datasets/open-r1/Mixture-of-Thoughts) | SFT | Regular SFT | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/sft_mot), [doc](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_dpo.html#configuration-for-sft) |
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| [HumanLLMs/Human-Like-DPO-Dataset](https://huggingface.co/datasets/HumanLLMs/Human-Like-DPO-Dataset) | DPO | Training based on prepared human preferences | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/dpo_humanlike), [doc](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_dpo.html) |
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| toy dataset | DPO | Training based on human-in-the-loop preference annotation | [example](https://github.com/modelscope/Trinity-RFT/tree/main/examples/dpo_human_in_the_loop), [doc](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_data_functionalities.html#example-human-in-the-loop) |
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‎docs/sphinx_doc/source_zh/index.rst‎

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