Xin He, Yongchao Deng, Liang Wen, Haosheng Zou, Xiangzheng Zhang, Qi An, Yunke Cai, Fenrui Xiao, Zhenyu Duan, Yimin Du, Junchen Liu, Lifu Tang, and 3 more
We lifted 13 functions out of this paper's own repositories and ran 9 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| Qihoo360/Light-R1 | canonical | 9 of 13 |
| Function | Status | Where it lives |
|---|---|---|
| build_ngram_lookup | Ran | Qihoo360/Light-R1/decontaminate/n_gram_check.py code served (permissive licence) · get_code("57f0335f22430956") |
| clean_text | Ran | Qihoo360/Light-R1/decontaminate/character_matching.py code served (permissive licence) · get_code("61619ba901c7b574") |
| count_unknown_letters_in_expr | Ran | Qihoo360/Light-R1/deepscaler-release/deepscaler/rewards/math_utils/utils.py code served (permissive licence) · get_code("368d3db6347269c3") |
| create_huggingface_actor | Ran | Qihoo360/Light-R1/deepscaler-release/verl/verl/utils/model.py code served (permissive licence) · get_code("360c671d6041ba3b") |
| normalize_string | Ran | Qihoo360/Light-R1/decontaminate/n_gram_check.py code served (permissive licence) · get_code("927fae1304011f13") |
| should_allow_eval | Ran | Qihoo360/Light-R1/deepscaler-release/deepscaler/rewards/math_utils/utils.py code served (permissive licence) · get_code("95f02454eddf082a") |
| union_tensor_dict | Ran | Qihoo360/Light-R1/deepscaler-release/verl/verl/protocol.py code served (permissive licence) · get_code("21331a58f93375e4") |
| unpad_dataproto | Ran | Qihoo360/Light-R1/deepscaler-release/verl/verl/protocol.py code served (permissive licence) · get_code("25f0ea3f460f6ce1") |
| word_ngrams | Ran | Qihoo360/Light-R1/decontaminate/n_gram_check.py code served (permissive licence) · get_code("b740be8e99a575cb") |
| get_huggingface_actor_config | Not yet run | Qihoo360/Light-R1/deepscaler-release/verl/verl/utils/model.py code served (permissive licence) · get_code("19472d4343d0a082") |
| get_weight_loader | Not yet run | Qihoo360/Light-R1/deepscaler-release/verl/verl/models/weight_loader_registry.py code served (permissive licence) · get_code("919cf310ad4c189a") |
| mathd_normalize_answer | Not yet run | Qihoo360/Light-R1/deepscaler-release/deepscaler/rewards/math_utils/utils.py code served (permissive licence) · get_code("ec578739f88adc22") |
| squeeze | Not yet run | Qihoo360/Light-R1/deepscaler-release/verl/verl/utils/model.py code served (permissive licence) · get_code("3b15e2ac7497c441") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This paper introduces Light-R1, an opensource suite for training long reasoning models using reproducible and cost-effective methodology. Given the proprietary nature of data used in the DeepSeek-R1 series, we develop an alternative approach leveraging exclusively public data and models. Our curriculum training progressively increases data difficulty, combined with multi-staged post-training. Our Light-R1-32B model, trained from Qwen2.5-32B-Instruct, outperforms DeepSeek-R1-Distill-Qwen-32B in math reasoning. Experimental results show that this curriculum approach becomes more effective when distinct, diverse datasets are available for different training stages: fine-tuning DeepSeek-R1-Distilled models (pre-tuned by DeepSeek team on proprietary data) with 3,000 challenging examples from our curriculum dataset yielded state-ofthe-art 7B and 14B models, while the 32B model, Light-R1-32B-DS performed comparably to QwQ-32B and DeepSeek-R1. Furthermore, we extend our work by applying GRPO on long reasoning models. Our final Light-R1-14B-DS achieves SOTA performance among 14B models in math, with AIME24 & 25 scores of 74.0 and 60.2 respectively, surpassing many 32B models and DeepSeek-R1-Distill-Llama-70B. Despite math-focused training, Light-R1-14B-DS demonstrates strong cross-domain generalization. Light-R1 represents a significant advancement in making sophisticated reasoning models more accessible and implementable in real-world applications. Our models, training data and code have been made available at https://github.com/Qihoo360/Light-R1.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2503.10460")
get_code_for_paper("2503.10460")
have("2503.10460")
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