@inproceedings{liu-etal-2026-rlpgr,
title = "{RLP}g{R}: Reinforcement Learning from Dependency-Aware Progress Rewards",
author = "Liu, Shaofan and
Yang, Tingyi and
Dou, Shihan and
Xi, Zhiheng and
Ye, Junjie and
Shang, Junlin and
Yang, Yuming and
Wang, Shaowen and
Chen, Jiayi and
Zhang, Jiazheng and
Zhang, Guoqiang and
Xie, Huaibing and
Zhou, Pluto and
Gui, Tao and
Zhang, Qi and
Huang, Xuanjing",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1745/",
pages = "37334--37364",
ISBN = "979-8-89176-462-0",
abstract = "Group-relative policy optimization (GRPO) provides a stable recipe for outcome-based RL on language model reasoning, but its credit assignment is coarse: it broadcasts a single trajectory advantage to every token, regardless of how individual steps contribute to the outcome. We call this the uniform-broadcast failure mode and address it with RLPgR, a dependency-aware rescaling of the existing GRPO advantage. RLPgR parses each rollout into a Progress Dependency Graph (PDG), where each node groups a contiguous span of steps into a milestone-level unit with a locally validatable artifact, and directed edges connect a node to the artifacts it depends on. Using the PDG, RLPgR rescales the group-relative advantage by a small, bounded, sign-preserving factor at the node level. This preserves the outcome-governed rollout direction while each step{'}s update better reflects its dependency role, avoiding the reward-hacking drift of densely shaped process rewards. To reduce PDG construction cost, we train an open Qwen3-8B progress annotator amortizing a one-time proprietary bootstrap. Across model families and sizes on five math reasoning benchmarks, RLPgR is competitive with strong outcome-RL and process-RL baselines under matched training budget. Three controlled studies (scaling-strength sensitivity, segment-level baselines, and PDG label perturbation) support the view that the gain comes from structured credit assignment rather than incidental tuning."
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<abstract>Group-relative policy optimization (GRPO) provides a stable recipe for outcome-based RL on language model reasoning, but its credit assignment is coarse: it broadcasts a single trajectory advantage to every token, regardless of how individual steps contribute to the outcome. We call this the uniform-broadcast failure mode and address it with RLPgR, a dependency-aware rescaling of the existing GRPO advantage. RLPgR parses each rollout into a Progress Dependency Graph (PDG), where each node groups a contiguous span of steps into a milestone-level unit with a locally validatable artifact, and directed edges connect a node to the artifacts it depends on. Using the PDG, RLPgR rescales the group-relative advantage by a small, bounded, sign-preserving factor at the node level. This preserves the outcome-governed rollout direction while each step’s update better reflects its dependency role, avoiding the reward-hacking drift of densely shaped process rewards. To reduce PDG construction cost, we train an open Qwen3-8B progress annotator amortizing a one-time proprietary bootstrap. Across model families and sizes on five math reasoning benchmarks, RLPgR is competitive with strong outcome-RL and process-RL baselines under matched training budget. Three controlled studies (scaling-strength sensitivity, segment-level baselines, and PDG label perturbation) support the view that the gain comes from structured credit assignment rather than incidental tuning.</abstract>
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%0 Conference Proceedings
%T RLPgR: Reinforcement Learning from Dependency-Aware Progress Rewards
%A Liu, Shaofan
%A Yang, Tingyi
%A Dou, Shihan
%A Xi, Zhiheng
%A Ye, Junjie
%A Shang, Junlin
%A Yang, Yuming
%A Wang, Shaowen
%A Chen, Jiayi
%A Zhang, Jiazheng
%A Zhang, Guoqiang
%A Xie, Huaibing
%A Zhou, Pluto
%A Gui, Tao
%A Zhang, Qi
%A Huang, Xuanjing
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F liu-etal-2026-rlpgr
%X Group-relative policy optimization (GRPO) provides a stable recipe for outcome-based RL on language model reasoning, but its credit assignment is coarse: it broadcasts a single trajectory advantage to every token, regardless of how individual steps contribute to the outcome. We call this the uniform-broadcast failure mode and address it with RLPgR, a dependency-aware rescaling of the existing GRPO advantage. RLPgR parses each rollout into a Progress Dependency Graph (PDG), where each node groups a contiguous span of steps into a milestone-level unit with a locally validatable artifact, and directed edges connect a node to the artifacts it depends on. Using the PDG, RLPgR rescales the group-relative advantage by a small, bounded, sign-preserving factor at the node level. This preserves the outcome-governed rollout direction while each step’s update better reflects its dependency role, avoiding the reward-hacking drift of densely shaped process rewards. To reduce PDG construction cost, we train an open Qwen3-8B progress annotator amortizing a one-time proprietary bootstrap. Across model families and sizes on five math reasoning benchmarks, RLPgR is competitive with strong outcome-RL and process-RL baselines under matched training budget. Three controlled studies (scaling-strength sensitivity, segment-level baselines, and PDG label perturbation) support the view that the gain comes from structured credit assignment rather than incidental tuning.
%U https://aclanthology.org/2026.findings-emnlp.1745/
%P 37334-37364
Markdown (Informal)
[RLPgR: Reinforcement Learning from Dependency-Aware Progress Rewards](https://aclanthology.org/2026.findings-emnlp.1745/) (Liu et al., Findings 2026)
ACL
- Shaofan Liu, Tingyi Yang, Shihan Dou, Zhiheng Xi, Junjie Ye, Junlin Shang, Yuming Yang, Shaowen Wang, Jiayi Chen, Jiazheng Zhang, Guoqiang Zhang, Huaibing Xie, Pluto Zhou, Tao Gui, Qi Zhang, and Xuanjing Huang. 2026. RLPgR: Reinforcement Learning from Dependency-Aware Progress Rewards. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 37334–37364, Budapest, Hungary. Association for Computational Linguistics.