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Small Reward Models via Backward Inference

arXiv:2602.13551v1 Announce Type: new Abstract: Reward models (RMs) play a central role throughout the language model (LM) pipeline, particularly in non-verifiable domains. However, the dominant LLM-as-a-Judge paradigm relies on the strong reasoning capabilities of large models, while alternative approaches require reference responses or explicit rubrics, limiting flexibility and broader accessibility. In this work, we propose FLIP (FLipped Inference for Prompt reconstruction), a reference-free and rubric-free reward modeling approach that reformulates reward modeling through backward inference: inferring the instruction that would most plausibly produce a given response. The similarity between the inferred and the original instructions is then used as the reward signal. Evaluations across four domains using 13 small language models show that FLIP outperforms LLM-as-a-Judge baselines by an average of 79.6%. Moreover, FLIP substantially improves downstream performance in extrinsic eval

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Yike Wang, Faeze Brahman, Shangbin Feng, Teng Xiao, Hannaneh Hajishirzi, Yulia Tsvetkov · · 1 min read · 2 views
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arXiv:2602.13551v1 Announce Type: new Abstract: Reward models (RMs) play a central role throughout the language model (LM) pipeline, particularly in non-verifiable domains. However, the dominant LLM-as-a-Judge paradigm relies on the strong reasoning capabilities of large models, while alternative approaches require reference responses or explicit rubrics, limiting flexibility and broader accessibility. In this work, we propose FLIP (FLipped Inference for Prompt reconstruction), a reference-free and rubric-free reward modeling approach that reformulates reward modeling through backward inference: inferring the instruction that would most plausibly produce a given response. The similarity between the inferred and the original instructions is then used as the reward signal. Evaluations across four domains using 13 small language models show that FLIP outperforms LLM-as-a-Judge baselines by an average of 79.6%. Moreover, FLIP substantially improves downstream performance in extrinsic evaluations under test-time scaling via parallel sampling and GRPO training. We further find that FLIP is particularly effective for longer outputs and robust to common forms of reward hacking. By explicitly exploiting the validation-generation gap, FLIP enables reliable reward modeling in downscaled regimes where judgment methods fail. Code available at https://github.com/yikee/FLIP.

Executive Summary

The article introduces FLIP, a novel approach to reward modeling in language models (LMs) that eliminates the need for reference responses or explicit rubrics. FLIP reformulates reward modeling through backward inference, inferring the instruction that would most plausibly produce a given response and using the similarity between the inferred and original instructions as the reward signal. Evaluations across four domains with 13 small language models show FLIP outperforming LLM-as-a-Judge baselines by an average of 79.6%. FLIP also improves downstream performance and is robust to reward hacking, offering a reliable method for reward modeling in downscaled regimes.

Key Points

  • ▸ FLIP is a reference-free and rubric-free reward modeling approach.
  • ▸ It uses backward inference to infer the instruction that would most plausibly produce a given response.
  • ▸ FLIP outperforms LLM-as-a-Judge baselines by an average of 79.6% across four domains.
  • ▸ It improves downstream performance and is robust to common forms of reward hacking.
  • ▸ FLIP is particularly effective for longer outputs and reliable in downscaled regimes.

Merits

Innovative Approach

FLIP introduces a novel method for reward modeling that does not rely on reference responses or explicit rubrics, making it more flexible and accessible.

Superior Performance

FLIP outperforms existing LLM-as-a-Judge baselines significantly, demonstrating its effectiveness in various domains.

Robustness

FLIP is robust to reward hacking and performs well with longer outputs, making it a reliable choice for reward modeling.

Demerits

Complexity

The backward inference process may introduce complexity in implementation and understanding, potentially limiting its immediate adoption.

Domain Specificity

While FLIP shows promise across multiple domains, its effectiveness may vary depending on the specific characteristics of the domain.

Expert Commentary

The introduction of FLIP represents a significant advancement in the field of reward modeling for language models. By eliminating the need for reference responses or explicit rubrics, FLIP addresses key limitations of existing methods and offers a more flexible and accessible approach. The method's superior performance, as demonstrated across multiple domains, underscores its potential to become a standard in the industry. However, the complexity of the backward inference process and potential domain specificity may pose challenges for immediate adoption. Despite these limitations, FLIP's robustness to reward hacking and effectiveness with longer outputs make it a valuable tool for enhancing the reliability and security of AI systems. The practical and policy implications of FLIP are substantial, with the potential to influence both industry practices and regulatory frameworks.

Recommendations

  • ✓ Further research should explore the scalability and adaptability of FLIP across a broader range of domains and use cases.
  • ✓ Industry stakeholders should consider integrating FLIP into their language model pipelines to leverage its superior performance and robustness.

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