Blockwise Advantage Estimation for Multi-Objective RL with Verifiable Rewards

• URL: http://arxiv.org/abs/2602.10231v1
• Date: Tue, 10 Feb 2026 19:22:37 GMT
• Title: Blockwise Advantage Estimation for Multi-Objective RL with Verifiable Rewards
• Authors: Kirill Pavlenko, Alexander Golubev, Simon Karasik, Boris Yangel,
• Abstract summary: Group Relative Policy Optimization (GRPO) assigns a single scalar advantage to all tokens in a completion.<n>For structured generations with explicit segments and objectives, this couples unrelated reward signals across segments, leading to objective interference and misattributed credit.<n>We propose Blockwise Advantage Estimation, a family of GRPO-compatible methods that assigns each objective its own advantage and applies it only to the tokens in the corresponding text block.
• Score: 39.489554597919145
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Group Relative Policy Optimization (GRPO) assigns a single scalar advantage to all tokens in a completion. For structured generations with explicit segments and objectives, this couples unrelated reward signals across segments, leading to objective interference and misattributed credit. We propose Blockwise Advantage Estimation, a family of GRPO-compatible methods that assigns each objective its own advantage and applies it only to the tokens in the corresponding text block, reducing reliance on hand-designed scalar rewards and scaling naturally to additional objectives. A key challenge is estimating advantages for later blocks whose rewards are conditioned on sampled prefixes; standard unbiased approaches require expensive nested rollouts from intermediate states. Concretely, we introduce an Outcome-Conditioned Baseline that approximates intermediate state values using only within-group statistics by stratifying samples according to a prefix-derived intermediate outcome. On math tasks with uncertainty estimation, our method mitigates reward interference, is competitive with a state-of-the-art reward-designed approach, and preserves test-time gains from confidence-weighted ensembling. More broadly, it provides a modular recipe for optimizing sequential objectives in structured generations without additional rollouts.

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