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Review regressions before expanding the test." ], "measure": "Held-out quality, peak memory, training time, and full compute cost to the target quality.", "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation." }, "builder_review": null, "arxiv_id": "2610.00574", "title": "Make Sparse Rewards Count: Density-Aware Reward Aggregation for Multi-Reward RL", "summary": "DARA replaces fixed or purely reward-wise-normalized multi-reward aggregation, such as GDPO weighting, with per-batch inverse-square-root weights based on active-group density. Its added cost is small compute for batch reward-activity statistics, but it requires an existing multi-reward RL loop with grouped rollouts, per-reward advantages, and monitorable reward signals. 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