Blockwise advantage estimation solves multi-objective RL interference by decoupling rewards across output sections.
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.
Blockwise Advantage Estimation decouples multi-objective rewards by computing separate advantages for different text blocks instead of one scalar.
GRPO's monolithic approach causes objective interference when different output sections serve different purposes in language model tasks.
BAE uses efficient baseline estimation for sequential blocks without expensive Monte Carlo rollouts, maintaining GRPO's computational efficiency.
The method requires clear structural decomposition of tasks into distinct blocks, limiting applicability to unstructured outputs.
Baseline estimation for later blocks remains approximate due to computational constraints of language model generation.
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