# Authors propose DEPO to filter training data before model rollouts [Canonical reading](https://escapemeridian.com/readings/authors-propose-depo-to-filter-training-data-before-model-a1565356) The authors of Difficulty-Estimated Policy Optimization (DEPO) propose an online Difficulty Estimator that assesses and filters training data before rollout, directing compute toward samples they consider to have higher learning potential. They argue this addresses a weakness in Group Relative Policy Optimization (GRPO): on very easy or very difficult problems, differences in group advantages can disappear, making the training signal vulnerable to noise. The authors report that, in their profiled configuration, DEPO produced rollouts approximately 1.86 times faster than DAPO and improved benchmark performance over GRPO at comparable training cost. Those results describe the authors’ reported setup; the abstract does not establish how well the approach transfers to other models or training settings. Escape Meridian interpretation: the proposed efficiency gain could matter to teams training reasoning models if it holds beyond that configuration, but its practical reach remains unknown. What results would DEPO achieve across other models, tasks, and training settings? Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims. ## Original source arXiv cs.AI · Article Original source date: Feb 6, 2026 (2026-02-06). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-14T05:03:40.386Z. First observed: 2026-10-28T04:26:36.638Z. Retrieved: 2026-10-30T06:01:16.856Z. - [Difficulty-Estimated Policy Optimization](https://arxiv.org/abs/2602.06375) ## Limits The abstract reports results in a profiled configuration but does not establish whether they transfer to other models, tasks, or training settings. The evidence is the paper's arXiv abstract; detailed methods and results are not included in the supplied text. ## Open question What results would DEPO achieve across other models, tasks, and training settings? ## Source-stated The authors propose DEPO, which assesses and filters training data before rollout to prioritize samples with high learning potential. > DEPO integrates an online Difficulty Estimator that dynamically assesses and filters training data before the rollout phase. - [Supporting source](https://arxiv.org/abs/2602.06375) ## Source-stated The authors say GRPO can have noisy training signals on problems that are too trivial or overly complex, where inter-group advantages disappear. > the disappearance of inter-group advantages makes the gradient signal susceptible to noise - [Supporting source](https://arxiv.org/abs/2602.06375) ## Source-stated In the profiled configuration, the authors report approximately 1.86 times faster rollouts than DAPO and improved benchmark performance over GRPO at comparable training cost. > DEPO achieves approximately 1.86x faster rollouts than DAPO in the profiled configuration, while improving benchmark performance over GRPO at comparable training cost. - [Supporting source](https://arxiv.org/abs/2602.06375) ## Escape Meridian interpretation If the reported efficiency gain holds across other training settings, it could matter to teams training reasoning models. > DEPO achieves approximately 1.86x faster rollouts than DAPO in the profiled configuration - [Supporting source](https://arxiv.org/abs/2602.06375) ## Source and contributor context Zhao, Yu, Jiang, Fan, Liu, Tianle, Zeng, Bo, Wang, Xintong, Teng, Jiaxuan, Liu, Yu, Du, Yichao · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Benchmark A defined set of tests used to compare systems. Results depend on the tasks and measures selected. - [Definition reference](https://arxiv.org/abs/2211.09110) ## Reasoning model A language model trained to work through a problem in written steps before it answers. - [Definition reference](https://en.wikipedia.org/wiki/Reasoning_model) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: f41d6dfd-6b78-49d6-89c7-99cafa89d2b4. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-14T05:03:40.386Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: da14dcd9548f5cd2bfffb93177d585c805ee04b9464975017094a97f7d8c84a1. Source text hash: cca1a9c5556577e2d04f953d504b11a1fb0427b5cd14d6c36a3d6ce219c5e8b4. ## Revision and source-check history ### Version 1 — 2026-11-14T05:03:40.386Z The authors of Difficulty-Estimated Policy Optimization (DEPO) propose an online Difficulty Estimator that assesses and filters training data before rollout, directing compute toward samples they consider to have higher learning potential. They argue this addresses a weakness in Group Relative Policy Optimization (GRPO): on very easy or very difficult problems, differences in group advantages can disappear, making the training signal vulnerable to noise. The authors report that, in their profiled configuration, DEPO produced rollouts approximately 1.86 times faster than DAPO and improved benchmark performance over GRPO at comparable training cost. Those results describe the authors’ reported setup; the abstract does not establish how well the approach transfers to other models or training settings. Escape Meridian interpretation: the proposed efficiency gain could matter to teams training reasoning models if it holds beyond that configuration, but its practical reach remains unknown. What results would DEPO achieve across other models, tasks, and training settings? ## Original source arXiv cs.AI · Article Original source date: Feb 6, 2026 (2026-02-06). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-14T05:03:40.386Z. First observed: 2026-10-28T04:26:36.638Z. Retrieved: 2026-10-30T06:01:16.856Z. - [Difficulty-Estimated Policy Optimization](https://arxiv.org/abs/2602.06375) ## Limits The abstract reports results in a profiled configuration but does not establish whether they transfer to other models, tasks, or training settings. The evidence is the paper's arXiv abstract; detailed methods and results are not included in the supplied text. ## Open question What results would DEPO achieve across other models, tasks, and training settings? ## Source-stated The authors propose DEPO, which assesses and filters training data before rollout to prioritize samples with high learning potential. > DEPO integrates an online Difficulty Estimator that dynamically assesses and filters training data before the rollout phase. - [Supporting source](https://arxiv.org/abs/2602.06375) ## Source-stated The authors say GRPO can have noisy training signals on problems that are too trivial or overly complex, where inter-group advantages disappear. > the disappearance of inter-group advantages makes the gradient signal susceptible to noise - [Supporting source](https://arxiv.org/abs/2602.06375) ## Source-stated In the profiled configuration, the authors report approximately 1.86 times faster rollouts than DAPO and improved benchmark performance over GRPO at comparable training cost. > DEPO achieves approximately 1.86x faster rollouts than DAPO in the profiled configuration, while improving benchmark performance over GRPO at comparable training cost. - [Supporting source](https://arxiv.org/abs/2602.06375) ## Escape Meridian interpretation If the reported efficiency gain holds across other training settings, it could matter to teams training reasoning models. > DEPO achieves approximately 1.86x faster rollouts than DAPO in the profiled configuration - [Supporting source](https://arxiv.org/abs/2602.06375) ## Source and contributor context Zhao, Yu, Jiang, Fan, Liu, Tianle, Zeng, Bo, Wang, Xintong, Teng, Jiaxuan, Liu, Yu, Du, Yichao · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Benchmark A defined set of tests used to compare systems. Results depend on the tasks and measures selected. - [Definition reference](https://arxiv.org/abs/2211.09110) ## Reasoning model A language model trained to work through a problem in written steps before it answers. - [Definition reference](https://en.wikipedia.org/wiki/Reasoning_model) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: f41d6dfd-6b78-49d6-89c7-99cafa89d2b4. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-14T05:03:40.386Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: da14dcd9548f5cd2bfffb93177d585c805ee04b9464975017094a97f7d8c84a1. Source text hash: cca1a9c5556577e2d04f953d504b11a1fb0427b5cd14d6c36a3d6ce219c5e8b4. ### uncertain — 2026-11-14T05:03:40.386Z — anthropic/claude-opus-5.5 Publication: f41d6dfd-6b78-49d6-89c7-99cafa89d2b4 attribution — pass: The method, the GRPO weakness and the speed and benchmark results are all attributed to the paper's authors. The reading does not present them as established facts. > In this paper, we propose Difficulty-Estimated Policy Optimization (DEPO) summary — uncertain: Central terms go unexplained for a general reader: rollout (the model generating candidate answers during training), DAPO, and 'group advantages'. The reading never says GRPO is a reinforcement-learning method used to train reasoning models such as DeepSeek-R1, so the stakes are unclear. It also drops the authors' own significance claim, that DEPO lowers the computational barrier to training reasoning models, and replaces it with an EM hedge. Smallest repair: add one plain-language context sentence and attribute the authors' barrier claim. Add term-library tooltips for rollout, GRPO and DAPO. > Our approach significantly lowers the computational barrier for training high-performance reasoning models, offering a more sustainable path for reasoning scaling. date — pass: originalPublished is 2026-02-06, which matches the v1 submission date. It is not the 27 Oct 2026 v2 revision or the October feed date. > \[Submitted on 6 Feb 2026 (v1), last revised 27 Oct 2026 (this version, v2)\] support — pass: Each source-stated claim matches the abstract, including the scope condition 'in the profiled configuration'. The interpretation is labeled as Escape Meridian's, kept conditional and backed by an excerpt. The limitations correctly note that only the abstract was supplied. > DEPO achieves approximately 1.86x faster rollouts than DAPO in the profiled configuration, while improving benchmark performance over GRPO at comparable training cost. media — pass: The selected media is the verified direct arXiv abstract page for this paper, which matches the source URL. No better source media was found. ## Recorded references Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter. No public references recorded here yet. [Reference data (JSON)](/readings/a1565356bc76ddbe78a4a7e9ff0f503c6a67a11b24f1616e6f49f850dd7de943/references.json) ## Corrections and challenges [Send a correction or an opposing source](https://escapemeridian.com/corrections/) [JSON record](https://escapemeridian.com/readings/authors-propose-depo-to-filter-training-data-before-model-a1565356.json)