repo_name stringlengths 8 65 | repo_url stringlengths 27 84 | license stringclasses 6
values | file_path stringlengths 6 85 | file_url stringlengths 83 181 | timestamp stringlengths 26 26 | reward_functions listlengths 1 20 | trainer_usages listlengths 1 23 |
|---|---|---|---|---|---|---|---|
dstackai/dstack | https://github.com/dstackai/dstack | Mozilla Public License 2.0 | examples/llms/deepseek/trl/amd/grpo_train.py | https://github.com/dstackai/dstack/blob/58181d1fe372488d9a64075c24d975935411f31d/examples/llms/deepseek/trl/amd/grpo_train.py | 2025-03-24T10:14:18.106059 | [
{
"name": "reward_len",
"code": "def reward_len(completions, **kwargs):\n return [abs(20 - len(completion)) for completion in completions]",
"label": "{\"label\": \"LENGTH_BASED\"}"
}
] | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "reward_len",
"args": "training_args",
"train_dataset": "dataset",
"eval_dataset": null,
"peft_config": null,
"reward_processing_classes": null,
"processing_class": ... |
philschmid/deep-learning-pytorch-huggingface | https://github.com/philschmid/deep-learning-pytorch-huggingface | MIT License | training/scripts/run_r1_grpo.py | https://github.com/philschmid/deep-learning-pytorch-huggingface/blob/59b37973074de90004d10e5ff636f98160c9743a/training/scripts/run_r1_grpo.py | 2025-03-24T10:14:24.890615 | [
{
"name": "format_reward_func (from list item 0)",
"code": "def format_reward_func(completions, target, **kwargs):\n \"\"\"\n Format: <think>...</think><answer>...</answer>\n Args:\n completions (list[str]): Generated outputs\n target (list[str]): Expected answers\n \n Retur... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model_args.model_name_or_path",
"reward_funcs": "[format_reward_func, equation_reward_func]",
"args": "training_args",
"train_dataset": "train_dataset",
"eval_dataset": "test_dataset",
"peft_config": "... |
huihuihenqiang/wechat-simulate-human | https://github.com/huihuihenqiang/wechat-simulate-human | Unknown | ft/deepseek_r1_train.py | https://github.com/huihuihenqiang/wechat-simulate-human/blob/26042f23d5c26501816a2d4ef498134e94349085/ft/deepseek_r1_train.py | 2025-03-24T10:14:27.153189 | [
{
"name": "mark_reward (from list item 0)",
"code": "def mark_reward(completions, **kwargs):\n responses = [completion[0]['content'] for completion in completions]\n return [mark_num(response) for response in responses]",
"label": "{\"label\": \"ANSWER_TYPE_VALIDATION\"}"
},
{
"name": "sof... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[mark_reward, soft_format_reward, hard_format_reward, digit_reward, correctness_reward]",
"args": "training_args",
"train_dataset": "data",
"eval_dataset": null,
"peft_config":... |
Doriandarko/MLX-GRPO | https://github.com/Doriandarko/MLX-GRPO | Unknown | mlx-grpo.py | https://github.com/Doriandarko/MLX-GRPO/blob/eaacf96e4ad464860144f52b9823408f0ae7c295/mlx-grpo.py | 2025-03-24T10:14:29.408549 | [
{
"name": "xmlcount_reward_func (from list item 0)",
"code": "def xmlcount_reward_func(completions, **kwargs) -> list[float]:\n contents = [completion[0]['content'] for completion in completions]\n return [count_xml(c) for c in contents]",
"label": "{\"label\": \"COMPUTATIONAL\"}"
},
{
"na... | [
{
"trainer_type": "MLXGRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[xmlcount_reward_func, soft_format_reward_func, strict_format_reward_func, int_reward_func, correctness_reward_func]",
"args": "config",
"train_dataset": "dataset",
"eval_dataset"... |
michaelhla/pro-1 | https://github.com/michaelhla/pro-1 | Apache License 2.0 | train/unsloth-grpo.py | https://github.com/michaelhla/pro-1/blob/e205302deb82e971311125869e74efa4feb636fc/train/unsloth-grpo.py | 2025-03-24T10:14:31.663908 | [
{
"name": "stability_reward_func (from list item 0)",
"code": "def stability_reward_func(prompts, completions, sequences, orig_stabs, **kwargs):\n \"\"\"Custom reward function for stability optimization with LLM-based soft rewards\"\"\"\n rewards = []\n direct_extraction_success = 0\n lm_applier... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[stability_reward_func]",
"args": "training_args",
"train_dataset": "train_dataset",
"eval_dataset": null,
"peft_config": null,
"reward_processing_classes": null,
"... |
transformerlab/transformerlab-api | https://github.com/transformerlab/transformerlab-api | GNU Affero General Public License v3.0 | transformerlab/plugins/unsloth_grpo_trainer/main.py | https://github.com/transformerlab/transformerlab-api/blob/b52bec9ee4707833a1f32cfe8130f6e7f618d52f/transformerlab/plugins/unsloth_grpo_trainer/main.py | 2025-03-24T10:14:33.958267 | [
{
"name": "xmlcount_reward_func (from list item 0)",
"code": "def xmlcount_reward_func(completions, **kwargs) -> list[float]:\n contents = [completion[0]['content'] for completion in completions]\n return [count_xml(c, start_thinking_string, end_thinking_string, start_answer_string, end_answer_string)... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[xmlcount_reward_func, correctness_reward_func, int_reward_func, strict_format_reward_func, soft_format_reward_func]",
"args": "args",
"train_dataset": "dataset",
"eval_dataset": nul... |
JinSeoung-Oh/Reference | https://github.com/JinSeoung-Oh/Reference | Unknown | Reasoning/ReasoningModels.py | https://github.com/JinSeoung-Oh/Reference/blob/e49eb8aea5ea65f0c3b687ece28f075d392d8156/Reasoning/ReasoningModels.py | 2025-03-24T10:14:36.212741 | [
{
"name": "custom_reward_func (from list item 0)",
"code": "def custom_reward_func(prompts, completions, answer, min_reasoning_length=10, **kwargs) -> list[float]:\n responses = [completion[0]['content'] for completion in completions]\n q = prompts[0][-1]['content']\n extracted_responses_answer = [... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[custom_reward_func]",
"args": "training_args",
"train_dataset": "dataset",
"eval_dataset": null,
"peft_config": null,
"reward_processing_classes": null,
"processin... |
lmassaron/Gemma-2-2B-IT-GRPO | https://github.com/lmassaron/Gemma-2-2B-IT-GRPO | Unknown | gemma-grpo.py | https://github.com/lmassaron/Gemma-2-2B-IT-GRPO/blob/23802c018aa1cb9ac74fa14bf2391769c44ebb2b/gemma-grpo.py | 2025-03-24T10:14:45.291361 | [
{
"name": "correctness_reward_func (from list item 0)",
"code": "def correctness_reward_func(completions, answer, **kwargs):\n \"\"\"Reward function that checks if the answer is correct.\"\"\"\n responses = [completion[0]['content'] for completion in completions]\n extracted_responses = [extract_la... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "params.MODEL_NAME",
"reward_funcs": "[correctness_reward_func, format_reward_func]",
"args": "training_args",
"train_dataset": "gsm8k_train",
"eval_dataset": null,
"peft_config": "peft_config",
"... |
yaosheng216/torch_demo | https://github.com/yaosheng216/torch_demo | Unknown | grpo/distillation_qwen.py | https://github.com/yaosheng216/torch_demo/blob/7c441b4fd4f4f71a62035761c206ed7aeba2439a/grpo/distillation_qwen.py | 2025-03-24T10:14:54.394361 | [
{
"name": "xmlcount_reward_func (from list item 0)",
"code": "def xmlcount_reward_func(completions, **kwargs) -> list[float]:\n contents = [completion[0]['content'] for completion in completions]\n return [count_xml(c) for c in contents]",
"label": "{\"label\": \"COMPUTATIONAL\"}"
},
{
"na... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[xmlcount_reward_func, soft_format_reward_func, strict_format_reward_func, int_reward_func, correctness_reward_func]",
"args": "training_args",
"train_dataset": "dataset",
"eval_data... |
erayalp808/GRPO-fine-tuning-turkish-gpt2-350m | https://github.com/erayalp808/GRPO-fine-tuning-turkish-gpt2-350m | Unknown | grpo_training.py | https://github.com/erayalp808/GRPO-fine-tuning-turkish-gpt2-350m/blob/5428820ca46cf074d97f957f126b3255a567c441/grpo_training.py | 2025-03-24T10:15:03.493678 | [
{
"name": "correctness_reward_func (from list item 0)",
"code": "def correctness_reward_func(prompts, completions, answer, **kwargs) -> list[float]:\n responses = [completion[0]['content'] for completion in completions]\n q = prompts[0][-1]['content']\n extracted_responses = [extract_final_answer(r... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "lora_model",
"reward_funcs": "[correctness_reward_func, strict_format_reward_func, soft_format_reward_func, xmlcount_reward_func]",
"args": "training_args",
"train_dataset": "dataset",
"eval_dataset": null,
... |
Asad-Shahab/sudokuLLM | https://github.com/Asad-Shahab/sudokuLLM | MIT License | finetune.py | https://github.com/Asad-Shahab/sudokuLLM/blob/4593b0f4b3d80f3afebf18653a279e6cea3b0068/finetune.py | 2025-03-24T10:15:24.265651 | [
{
"name": "xmlcount_reward_func (from list item 0)",
"code": "def xmlcount_reward_func(completions, **kwargs) -> list[float]:\n \"\"\"Reward function for XML formatting details.\"\"\"\n contents = [completion[0]['content'] for completion in completions]\n return [count_xml(c) for c in contents]",
... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[xmlcount_reward_func, soft_format_reward_func, strict_format_reward_func, int_reward_func, correctness_reward_func]",
"args": "training_args",
"train_dataset": "dataset",
"eval_data... |
alxndrTL/gpu-rl | https://github.com/alxndrTL/gpu-rl | Unknown | grpo_gsm8k.py | https://github.com/alxndrTL/gpu-rl/blob/1f2bd13c9864049ec94e356f20f0ffb7a1f4b1e3/grpo_gsm8k.py | 2025-03-24T10:15:26.599891 | [
{
"name": "format_reasoning_reward (from list item 0)",
"code": "def format_reasoning_reward(prompts, completions, answer, **kwargs) -> list[float]:\n parsed_responses = parse_responses(completions)\n rewards = [0.5 if r['thinking_content'] and r['response'] else 0.0 for r in parsed_responses]\n re... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model_args.model_name_or_path",
"reward_funcs": "[format_reasoning_reward, format_number_reward, accuracy_reward, log_rewards]",
"args": "training_args",
"train_dataset": "data",
"eval_dataset": null,
... |
Sam-de-Ham/finetuning-tests | https://github.com/Sam-de-Ham/finetuning-tests | Unknown | full_training_freeze.py | https://github.com/Sam-de-Ham/finetuning-tests/blob/7617ee1361314a054f353d2764affb6ace27ec50/full_training_freeze.py | 2025-03-24T10:15:28.888442 | [
{
"name": "reward_len",
"code": "def reward_len(completions, **kwargs):\n return [-abs(20 - len(completion)) for completion in completions]",
"label": "{\"label\": \"LENGTH_BASED\"}"
}
] | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "'deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B'",
"reward_funcs": "reward_len",
"args": "training_args",
"train_dataset": "dataset",
"eval_dataset": null,
"peft_config": null,
"reward_processing_clas... |
Sam-de-Ham/finetuning-tests | https://github.com/Sam-de-Ham/finetuning-tests | Unknown | full_training_simple.py | https://github.com/Sam-de-Ham/finetuning-tests/blob/7617ee1361314a054f353d2764affb6ace27ec50/full_training_simple.py | 2025-03-24T10:15:31.105297 | [
{
"name": "reward_len",
"code": "def reward_len(completions, **kwargs):\n return [-abs(20 - len(completion)) for completion in completions]",
"label": "{\"label\": \"LENGTH_BASED\"}"
}
] | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "'deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B'",
"reward_funcs": "reward_len",
"args": "training_args",
"train_dataset": "dataset",
"eval_dataset": null,
"peft_config": null,
"reward_processing_clas... |
summerspringwei/alpaca-lora-decompilation | https://github.com/summerspringwei/alpaca-lora-decompilation | Apache License 2.0 | models/llmcompiler/grpo_example.py | https://github.com/summerspringwei/alpaca-lora-decompilation/blob/3d5fb5344992dd9c6e8a6447feee89dc889921fd/models/llmcompiler/grpo_example.py | 2025-03-24T10:15:37.883093 | [
{
"name": "reward_len",
"code": "def reward_len(completions, **kwargs):\n return [-abs(20 - len(completion)) for completion in completions]",
"label": "{\"label\": \"LENGTH_BASED\"}"
}
] | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "'Qwen/Qwen2-0.5B-Instruct'",
"reward_funcs": "reward_len",
"args": "training_args",
"train_dataset": "dataset",
"eval_dataset": null,
"peft_config": null,
"reward_processing_classes": null,
... |
meetrais/LLM-Fine-Tuning | https://github.com/meetrais/LLM-Fine-Tuning | Unknown | Qwen2.5_3B_GRPO.py | https://github.com/meetrais/LLM-Fine-Tuning/blob/d5e226e401894795c38e671fef4e117254cfeb51/Qwen2.5_3B_GRPO.py | 2025-03-24T10:15:47.125877 | [
{
"name": "xmlcount_reward_func (from list item 0)",
"code": "def xmlcount_reward_func(completions, **kwargs) -> list[float]:\n contents = [completion[0]['content'] for completion in completions]\n return [count_xml(c) for c in contents]",
"label": "{\"label\": \"ANSWER_TYPE_VALIDATION\"}"
},
... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[xmlcount_reward_func, soft_format_reward_func, strict_format_reward_func, int_reward_func, correctness_reward_func]",
"args": "training_args",
"train_dataset": "dataset",
"eval_data... |
MarcoTuc/xent | https://github.com/MarcoTuc/xent | Unknown | llama-grpo-xent/lab.py | https://github.com/MarcoTuc/xent/blob/2a46ba203123eda2e8f195025309af53c0899555/llama-grpo-xent/lab.py | 2025-03-24T10:15:56.616591 | [
{
"name": "dummy_reward (from list item 0)",
"code": "def dummy_reward(completions, **kwargs):\n responses = [completion[0]['content'] for completion in completions]\n print(f'{len(responses)} completions have been produced')\n for response in responses:\n print(response)\n print('\\n... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[dummy_reward]",
"args": "training_args",
"train_dataset": "dataset['train']",
"eval_dataset": null,
"peft_config": null,
"reward_processing_classes": null,
"proces... |
Sam-de-Ham/finetuning-tests | https://github.com/Sam-de-Ham/finetuning-tests | Unknown | full_training_simple_grpo.py | https://github.com/Sam-de-Ham/finetuning-tests/blob/7617ee1361314a054f353d2764affb6ace27ec50/full_training_simple_grpo.py | 2025-03-24T10:16:01.156066 | [
{
"name": "reward_len",
"code": "def reward_len(completions, **kwargs):\n return [-abs(20 - len(completion)) for completion in completions]",
"label": "{\"label\": \"LENGTH_BASED\"}"
}
] | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "reward_len",
"args": "training_args",
"train_dataset": "dataset",
"eval_dataset": null,
"peft_config": null,
"reward_processing_classes": null,
"processing_class": ... |
The-Swarm-Corporation/AgentGym | https://github.com/The-Swarm-Corporation/AgentGym | MIT License | grpo_example_two.py | https://github.com/The-Swarm-Corporation/AgentGym/blob/baa5184fdbdc48bd64f5bde17909fa8c482c2851/grpo_example_two.py | 2025-03-24T10:16:07.952736 | [
{
"name": "reward_len",
"code": "def reward_len(completions, **kwargs):\n return [abs(20 - len(completion)) for completion in completions]",
"label": "{\"label\": \"LENGTH_BASED\"}"
}
] | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "'Qwen/Qwen2-0.5B-Instruct'",
"reward_funcs": "reward_len",
"args": "training_args",
"train_dataset": "dataset",
"eval_dataset": null,
"peft_config": null,
"reward_processing_classes": null,
... |
haoruilee/Awesome-GRPO-training-example | https://github.com/haoruilee/Awesome-GRPO-training-example | Unknown | GRPO-Llama-1B.py | https://github.com/haoruilee/Awesome-GRPO-training-example/blob/1a0cf86a50ed4d4602a1b28dbe44c23a83573a11/GRPO-Llama-1B.py | 2025-03-24T10:16:12.544588 | [
{
"name": "xmlcount_reward_func (from list item 0)",
"code": "def xmlcount_reward_func(completions, **kwargs) -> list[float]:\n contents = [completion[0]['content'] for completion in completions]\n return [count_xml(c) for c in contents]",
"label": "{\"label\": \"COMPUTATIONAL\"}"
},
{
"na... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[xmlcount_reward_func, soft_format_reward_func, strict_format_reward_func, int_reward_func, correctness_reward_func]",
"args": "training_args",
"train_dataset": "dataset",
"eval_data... |
xiiiiiiiiii/strategicLearning | https://github.com/xiiiiiiiiii/strategicLearning | Unknown | train_grpo_gsm8k.py | https://github.com/xiiiiiiiiii/strategicLearning/blob/f92d0b57e9f7727e0cdad8a5f3ee04b163071ab3/train_grpo_gsm8k.py | 2025-03-24T10:16:14.791293 | [
{
"name": "xmlcount_reward_func (from list item 0)",
"code": "def xmlcount_reward_func(completions, **kwargs) -> list[float]:\n contents = [completion[0]['content'] for completion in completions]\n return [count_xml(c) for c in contents]",
"label": "{\"label\": \"COMPUTATIONAL\"}"
},
{
"na... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[xmlcount_reward_func, soft_format_reward_func, strict_format_reward_func, int_reward_func, correctness_reward_func]",
"args": "training_args",
"train_dataset": "dataset",
"eval_data... |
datawhalechina/unlock-deepseek | https://github.com/datawhalechina/unlock-deepseek | Unknown | Datawhale-R1/train_Datawhale-R1_unsloth.py | https://github.com/datawhalechina/unlock-deepseek/blob/7bfaaf6f93dcf2249525392d5310881a58f6f79b/Datawhale-R1/train_Datawhale-R1_unsloth.py | 2025-03-24T10:16:21.520168 | [
{
"name": "format_reward_func (from list item 0)",
"code": "def format_reward_func(completions, **kwargs):\n \"\"\"\n 格式奖励函数，检查模型输出格式是否匹配: <think>...</think><answer>...</answer>\n\n 参数:\n completions (list[str]): 生成的输出\n 返回:\n list[float]: 奖励分数\n \"\"\"\n rewards = []\n fo... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[format_reward_func, equation_reward_func]",
"args": "training_args",
"train_dataset": "train_dataset",
"eval_dataset": "test_dataset",
"peft_config": null,
"reward_proce... |
Manto/chess-reasoning-zero | https://github.com/Manto/chess-reasoning-zero | MIT License | qwen-1.5b.countdown.py | https://github.com/Manto/chess-reasoning-zero/blob/a887574cccdee0a752c80181ce3d6f428acbd52a/qwen-1.5b.countdown.py | 2025-03-24T10:16:23.784558 | [
{
"name": "countdown_reward_func",
"code": "def countdown_reward_func(prompts, completions, ground_truth, **kwargs) -> list[float]:\n scores = []\n for prompt, completion, truth in zip(prompts, completions, ground_truth):\n score = compute_score(completion[0]['content'], truth)\n scores.... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "countdown_reward_func",
"args": "training_args",
"train_dataset": "train_dataset",
"eval_dataset": "test_dataset",
"peft_config": "lora_config",
"reward_processing_classe... |
summerspringwei/alpaca-lora-decompilation | https://github.com/summerspringwei/alpaca-lora-decompilation | Apache License 2.0 | models/llmcompiler/grpo_exebench.py | https://github.com/summerspringwei/alpaca-lora-decompilation/blob/3d5fb5344992dd9c6e8a6447feee89dc889921fd/models/llmcompiler/grpo_exebench.py | 2025-03-24T10:16:28.246568 | [
{
"name": "reward_compilation",
"code": "def reward_compilation(completions, **kwargs):\n original_input = [{} for _ in range(len(completions))]\n predict_list_length = []\n for k, v in kwargs.items():\n for i in range(len(v)):\n original_input[i][k] = v[i]\n validation_list = ... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model_path",
"reward_funcs": "reward_compilation",
"args": "training_args",
"train_dataset": "exebench_dataset",
"eval_dataset": null,
"peft_config": "lora_config",
"reward_processing_classes": n... |
Oxen-AI/GRPO-With-Cargo-Feedback | https://github.com/Oxen-AI/GRPO-With-Cargo-Feedback | MIT License | train.py | https://github.com/Oxen-AI/GRPO-With-Cargo-Feedback/blob/11d0f570898f5764d9a366898ccb3da4c745a378/train.py | 2025-03-24T10:16:32.752977 | [
{
"name": "cargo_build_reward_func (from list item 0)",
"code": "@experiment.log(f'cargo_build_rewards.jsonl')\ndef cargo_build_reward_func(prompts, completions, **kwargs) -> list[float]:\n responses = [completion[0]['content'] for completion in completions]\n extracted_answers = [extract_rust_code(r)... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[cargo_build_reward_func, cargo_clippy_reward_func, cargo_test_reward_func, non_empty_reward_func, test_block_count_reward_func, tests_have_asserts_reward_func]",
"args": "training_args",
... |
awdemos/awdemos | https://github.com/awdemos/awdemos | Unknown | demos/llm/alpha_maze_finder_grpo/alphamaze_solver.py | https://github.com/awdemos/awdemos/blob/f59b9335803e762618c92ec7b6e655a693607555/demos/llm/alpha_maze_finder_grpo/alphamaze_solver.py | 2025-03-24T10:16:37.352898 | [
{
"name": "maze_reward (from list item 0)",
"code": "def maze_reward(completions, prompts, **kwargs):\n rewards = []\n for completion in completions:\n game = MazeGame()\n moves = completion.split()\n for move in moves:\n _, done = game.move(move)\n if done:\... | [
{
"trainer_type": "CustomGRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[maze_reward]",
"args": "training_args",
"train_dataset": "train_dataset",
"eval_dataset": null,
"peft_config": null,
"reward_processing_classes": null,
"proc... |
HarleyCoops/TrainingRun | https://github.com/HarleyCoops/TrainingRun | Unknown | grpo_demo.py | https://github.com/HarleyCoops/TrainingRun/blob/371054d5438de5f97e2b54d8bdfd8deebbd3fe85/grpo_demo.py | 2025-03-24T10:16:39.741640 | [
{
"name": "xmlcount_reward_func (from list item 0)",
"code": "def xmlcount_reward_func(completions, **kwargs) -> list[float]:\n contents = [completion[0]['content'] for completion in completions]\n return [count_xml(c) for c in contents]",
"label": "{\"label\": \"COMPUTATIONAL\"}"
},
{
"na... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[xmlcount_reward_func, soft_format_reward_func, strict_format_reward_func, int_reward_func, correctness_reward_func]",
"args": "training_args",
"train_dataset": "dataset",
"eval_data... |
erfanzar/EasyDeL | https://github.com/erfanzar/EasyDeL | Apache License 2.0 | easydel/scripts/finetune/gsm8k_grpo.py | https://github.com/erfanzar/EasyDeL/blob/64a77804783cb790bff1f8c744163915f55aea5f/easydel/scripts/finetune/gsm8k_grpo.py | 2025-03-24T10:16:46.626204 | [
{
"name": "xmlcount_reward_func (from list item 0)",
"code": "def xmlcount_reward_func(completions, **kwargs) -> list[float]:\n contents = [completion[0]['content'] for completion in completions]\n return [count_xml(c) for c in contents]",
"label": "{\"label\": \"COMPUTATIONAL\"}"
},
{
"na... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[xmlcount_reward_func, soft_format_reward_func, strict_format_reward_func, int_reward_func, correctness_reward_func]",
"args": null,
"train_dataset": "train_dataset",
"eval_dataset":... |
benglard/consciousness | https://github.com/benglard/consciousness | Unknown | llm_safety.py | https://github.com/benglard/consciousness/blob/dc7e58655c53bb34d2bc9c1b6fb0c2f26a77b339/llm_safety.py | 2025-03-24T10:16:48.956696 | [
{
"name": "xmlcount_reward_func (from list item 0)",
"code": "def xmlcount_reward_func(completions, **kwargs) -> list[float]:\n contents = [completion[0]['content'] for completion in completions]\n return [count_xml(c) for c in contents]",
"label": "{\"label\": \"LENGTH_BASED\"}"
},
{
"nam... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[xmlcount_reward_func, soft_format_reward_func, strict_format_reward_func, int_reward_func, correctness_reward_func, smol_model_predictor]",
"args": "training_args",
"train_dataset": "data... |
nnebp/GPRO-s-game-of-life | https://github.com/nnebp/GPRO-s-game-of-life | Unknown | train_gsm8k_mps.py | https://github.com/nnebp/GPRO-s-game-of-life/blob/169c46a84cde5f6deb941bed685fdab0ffd1e11b/train_gsm8k_mps.py | 2025-03-24T10:16:51.195643 | [
{
"name": "correctness_reward (from list item 0)",
"code": "def correctness_reward(prompts, completions, answer, **kwargs):\n \"\"\"Reward function for correct answers\"\"\"\n responses = [completion[0]['content'] for completion in completions]\n extracted = [extract_xml_answer(r) for r in response... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "args.model_name",
"reward_funcs": "[correctness_reward, format_reward, numeric_answer_reward]",
"args": "training_args",
"train_dataset": "dataset",
"eval_dataset": null,
"peft_config": "peft_config",
... |
jianzhnie/Open-R1 | https://github.com/jianzhnie/Open-R1 | Apache License 2.0 | examples/grpo_gsm8k.py | https://github.com/jianzhnie/Open-R1/blob/cbcaa40cf795a99a394db4806685018d06452c23/examples/grpo_gsm8k.py | 2025-03-24T10:16:53.561361 | [
{
"name": "xmlcount_reward_func (from list item 0)",
"code": "def xmlcount_reward_func(completions, **kwargs) -> list[float]:\n contents = [completion[0]['content'] for completion in completions]\n return [count_xml(c) for c in contents]",
"label": "{\"label\": \"COMPUTATIONAL\"}"
},
{
"na... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[xmlcount_reward_func, soft_format_reward_func, strict_format_reward_func, int_reward_func, correctness_reward_func]",
"args": "training_args",
"train_dataset": "dataset",
"eval_data... |
jiangqx0225/llm_run_file | https://github.com/jiangqx0225/llm_run_file | Unknown | unsloth_grpo.py | https://github.com/jiangqx0225/llm_run_file/blob/c698e03108035ef3511df5afcc7f2029d25e90a7/unsloth_grpo.py | 2025-03-24T10:16:55.859060 | [
{
"name": "xmlcount_reward_func (from list item 0)",
"code": "def xmlcount_reward_func(completions, **kwargs) -> list[float]:\n contents = [completion[0]['content'] for completion in completions]\n return [count_xml(c) for c in contents]",
"label": "{\"label\": \"COMPUTATIONAL\"}"
},
{
"na... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[xmlcount_reward_func, soft_format_reward_func, strict_format_reward_func, int_reward_func, correctness_reward_func]",
"args": "training_args",
"train_dataset": "dataset",
"eval_data... |
idanshen/multi_ref | https://github.com/idanshen/multi_ref | Unknown | gsm8k_grpo.py | https://github.com/idanshen/multi_ref/blob/53c9484f9963d0eb6c320eb7741ca08018aaa350/gsm8k_grpo.py | 2025-03-24T10:17:00.417588 | [
{
"name": "xmlcount_reward_func (from list item 0)",
"code": "def xmlcount_reward_func(completions, **kwargs) -> list[float]:\n contents = [completion[0]['content'] for completion in completions]\n return [count_xml(c) for c in contents]",
"label": "{\"label\": \"COMPUTATIONAL\"}"
},
{
"na... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[xmlcount_reward_func, soft_format_reward_func, strict_format_reward_func, int_reward_func, correctness_reward_func]",
"args": "training_args",
"train_dataset": "dataset",
"eval_data... |
yhfgyyf/GRPO_script | https://github.com/yhfgyyf/GRPO_script | Unknown | grpo_bleu.py | https://github.com/yhfgyyf/GRPO_script/blob/af9b2066201156eb07b270f60ccb50b552611249/grpo_bleu.py | 2025-03-24T10:17:02.749926 | [
{
"name": "assistant_format_count_reward (from list item 0)",
"code": "def assistant_format_count_reward(completions, **kwargs) -> list[float]:\n contents = [completion[0]['content'] for completion in completions]\n return [count_assistant_format(c) for c in contents]",
"label": "{\"label\": \"FOR... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[assistant_format_count_reward, assistant_format_reward_func, soft_assistant_format_reward_func, bleu_reward_func]",
"args": "training_args",
"train_dataset": "dataset",
"eval_datase... |
Legionof7/GRPOdx | https://github.com/Legionof7/GRPOdx | Unknown | Tic Tac Toe Game.py | https://github.com/Legionof7/GRPOdx/blob/b039810b169606493a1e3202dbf1b4d9cec02942/Tic%20Tac%20Toe%20Game.py | 2025-03-24T10:17:18.716467 | [
{
"name": "game_reward (from list item 0)",
"code": "def game_reward(completions, **kwargs) -> list[float]:\n return [0.0 for completion in completions]",
"label": "{\"label\": \"LENGTH_BASED\"}"
},
{
"name": "game_reward (from [game_reward])",
"code": "def game_reward(completions, **kwar... | [
{
"trainer_type": "CustomGRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[game_reward]",
"args": "training_args",
"train_dataset": "train_dataset",
"eval_dataset": null,
"peft_config": null,
"reward_processing_classes": null,
"proc... |
avinashreddydev/low-thinking | https://github.com/avinashreddydev/low-thinking | Apache License 2.0 | src/grpo_train_math.py | https://github.com/avinashreddydev/low-thinking/blob/95a2a8b79d7a863174e5ed33ed199a4116490f8a/src/grpo_train_math.py | 2025-03-24T10:17:20.948825 | [
{
"name": "format_reward_func (from list item 0)",
"code": "def format_reward_func(completions, **kwargs) -> list[float]:\n \"\"\"Reward function that checks if the completion has the correct format.\"\"\"\n pattern = '^<reasoning>(?:(?!</reasoning>).)*</reasoning>\\\\n<answer>(?:(?!</answer>).)*</ans... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[format_reward_func, correctness_reward_func]",
"args": "training_args",
"train_dataset": "dataset_train",
"eval_dataset": "dataset_test",
"peft_config": null,
"reward_pr... |
zzlzero/CodeLess | https://github.com/zzlzero/CodeLess | Unknown | run_grpo.py | https://github.com/zzlzero/CodeLess/blob/b2cb84a16a1764945fd93f4ca6d7fb39a55858b1/run_grpo.py | 2025-03-24T10:17:23.255479 | [
{
"name": "len_reward_func (from list item 0)",
"code": "def len_reward_func(completions, **kwargs):\n rewards = []\n max_len = max((len(completion) for completion in completions))\n for completion in completions:\n generation = task.postprocess_generation(completion)\n rewards.append... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model_args.model_name_or_path",
"reward_funcs": "[len_reward_func, correct_code_reward_func]",
"args": "training_args",
"train_dataset": "train_dataset",
"eval_dataset": "test_dataset",
"peft_config": ... |
menloresearch/visual-thinker | https://github.com/menloresearch/visual-thinker | Unknown | training/grpo_stage.py | https://github.com/menloresearch/visual-thinker/blob/bb74ee6fbf72b34321edcf2bb958921f694ab622/training/grpo_stage.py | 2025-03-24T10:17:27.798388 | [
{
"name": "xmlcount_reward_func (from list item 0)",
"code": "def xmlcount_reward_func(completions, **kwargs) -> List[float]:\n \"\"\"\n Reward function based on proper XML tag usage.\n \n Args:\n completions: Model completions\n \n Returns:\n List of reward scores\n \... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[xmlcount_reward_func, int_reward_func, correctness_reward_func]",
"args": "training_args",
"train_dataset": "dataset",
"eval_dataset": null,
"peft_config": null,
"reward... |
vpareek2/llm-experiments | https://github.com/vpareek2/llm-experiments | MIT License | llama-r1/grpo_trl.py | https://github.com/vpareek2/llm-experiments/blob/188644b99675411b0368d92c0cd29ddec0a0821f/llama-r1/grpo_trl.py | 2025-03-24T10:17:30.134516 | [
{
"name": "xmlcount_reward_func (from list item 0)",
"code": "def xmlcount_reward_func(completions, **kwargs) -> list[float]:\n contents = [completion[0]['content'] for completion in completions]\n return [count_xml(c) for c in contents]",
"label": "{\"label\": \"COMPUTATIONAL\"}"
},
{
"na... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[xmlcount_reward_func, soft_format_reward_func, strict_format_reward_func, int_reward_func, correctness_reward_func]",
"args": "training_args",
"train_dataset": "dataset",
"eval_data... |
bbirdxr/GRPO-Qwen2.5-7B-Medicine | https://github.com/bbirdxr/GRPO-Qwen2.5-7B-Medicine | Unknown | trl_grpo.py | https://github.com/bbirdxr/GRPO-Qwen2.5-7B-Medicine/blob/7617ccef6880d8cc13137df8def964b619143665/trl_grpo.py | 2025-03-24T10:17:32.445182 | [
{
"name": "reward_think_ratio (from list item 0)",
"code": "def reward_think_ratio(completions, **kwargs):\n scores = []\n for completion in completions:\n think_count = completion.count('<think>')\n think_end_count = completion.count('</think>')\n score = -abs(think_count - think... | [
{
"trainer_type": "GRPOTrainer",
"args": [],
"kwargs": {
"model": "model",
"reward_funcs": "[reward_think_ratio, similarity_sentence_score, correct_score]",
"args": "training_args",
"train_dataset": "train_dataset",
"eval_dataset": "test_dataset",
"peft_config": "peft... |
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