Temper and Tilt Lead to SLOP: Reward Hacking Mitigation with Inference-Time Alignment
Researchers propose SLOP, a calibration method for combining multiple reward models at inference time to reduce reward hacking while maintaining alignment quality. By adjusting reference-model temperature and weighting ensemble predictions as a sharpened logarithmic opinion pool, the technique sidesteps expensive reinforcement learning retraining cycles and adapts dynamically as alignment objectives shift. This matters because it lowers the operational cost of keeping deployed models aligned as safety standards evolve, making continual alignment more practical for resource-constrained teams.
Modelwire context
ExplainerSLOP's key insight is that you don't need to retrain the underlying policy to reduce reward hacking. By dynamically reweighting multiple reward models at inference time using temperature-adjusted logarithmic opinion pooling, teams can adapt to shifting safety standards without the computational and data overhead of full RL cycles.
This complements the multi-objective RL work from earlier this month (Reward-Decorrelated Policy Optimization), which tackled instability during training by normalizing heterogeneous reward signals. Where RDPO operates upstream during policy optimization, SLOP operates downstream at inference, offering a lower-friction alternative when retraining isn't feasible. The two represent different points on a spectrum: RDPO for teams building models from scratch with mixed objectives, SLOP for teams managing already-deployed systems facing evolving alignment requirements. Neither requires architectural changes, but they solve different deployment constraints.
If major labs adopt SLOP-style inference-time ensembling as a standard safety practice within the next six months, watch whether they publish ablations showing which reward model combinations actually reduce harmful outputs in adversarial settings (not just benchmark scores). If those ablations don't materialize, the method may be optimizing for calibration metrics rather than genuine safety gains.
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