ReCal: New Framework Improves Reinforcement Learning-Based Routing for Multiple Large Language Models
Researchers have introduced ReCal, a reward calibration framework designed to improve how reinforcement learning-based systems route queries across multiple large language models. Current RL-based routing methods struggle with inconsistent reward signals across tasks of varying difficulty, leading to biased optimization that favors easier samples. ReCal addresses this by decomposing rewards hierarchically and applying variance-aware reweighting, with experiments across seven datasets showing consistent performance and stability gains.
A preprint posted to arXiv presents ReCal (Reward Calibration), a framework targeting a key weakness in reinforcement learning-based LLM routing systems. LLM routing refers to dynamically selecting which language model or reasoning strategy to apply to a given query, aiming to exploit the complementary strengths of different models. The core problem ReCal addresses is that aggregating multiple objectives—such as correctness and formatting—into a single scalar reward creates ambiguous credit assignment and conflicting optimization signals. Additionally, reward variability across instances causes the training process to disproportionately favor simpler, higher-reward samples over more informative ones. ReCal introduces a hierarchical reward decomposition mechanism with component-wise advantage estimation to separate these objectives, alongside a distribution-aware optimization strategy using variance-aware reweighting and per-dataset normalization. Experiments conducted on seven datasets demonstrate that ReCal improves both routing performance and training stability compared to baseline methods. Code has been made publicly available by the authors.
What's missing
The generalizability of ReCal to open-ended or non-benchmarked tasks remains an open question, as all seven evaluation datasets are presumably structured benchmarks. Computational overhead introduced by the calibration mechanisms relative to standard RL routing is not discussed in the abstract.
What different sources said
- arXiv cs.AICenter
ReCal: Reward Calibration for RL-based LLM Routing
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