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All Roads Lead to Likelihood: The Value of Reinforcement Learning in Fine-Tuning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
reinforcement learningRLHFfine-tuning
Abstract:

From a first-principles perspective, it may seem odd that the strongest results in foundation model fine-tuning (FT) are achieved via a relatively complex, two-stage training procedure. Specifically, one first trains a reward model (RM) on some dataset (e.g., human preferences) before using it to provide online feedback as part of a downstream reinforcement learning (RL) procedure, rather than directly optimizing the policy parameters on said dataset via offline maximum likelihood estimation. In fact, from an information-theoretic perspective, we can only lose information via passing through a reward model and cannot create any new information via on-policy sampling. To explain this discrepancy, we scrutinize several hypotheses on the value of RL in FT through both theoretical and empirical lenses. Of the hypotheses considered, we find the most support for the explanation that on problems with a generation-verification gap, (1) it is relatively easy to learn the relatively simple RM (verifier) from the preference data. Then, (2) the downstream RL procedure only returns policies (generators) that are optimal for such relatively simple verifiers. Thus, end-to-end, two-stage online FT only has to search over a reduced subset of the full space of policies, requiring less data than offline FT.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper investigates why reinforcement learning fine-tuning outperforms direct supervised learning on preference data, proposing a generation-verification gap hypothesis. It occupies the 'Information-Theoretic and First-Principles Analysis' leaf within the taxonomy, where it is currently the sole member. This leaf sits under 'Theoretical Foundations and Comparative Analysis', a branch containing six survey papers and three empirical comparison studies across neighboring leaves. The sparse population of this specific leaf suggests that rigorous information-theoretic examinations of RL fine-tuning mechanisms remain relatively underexplored compared to algorithmic innovations or domain applications.

The taxonomy reveals a field organized around theoretical analysis, algorithmic development, and application domains. The paper's leaf neighbors 'Empirical Comparative Studies' (two papers comparing fine-tuning paradigms) and 'Survey and Tutorial Literature' (six comprehensive reviews). Nearby branches include 'Algorithmic Innovations' with dense subcategories on reward modeling and policy optimization, and 'Offline-to-Online RL' addressing pre-training integration. The scope note for the paper's leaf explicitly excludes empirical comparisons without theoretical grounding, positioning this work as foundational analysis rather than method engineering. Its focus on generation-verification gaps and information flow through reward models distinguishes it from neighboring algorithmic work.

Among thirty candidates examined, each of the three contributions shows at least one potentially overlapping prior work. The theoretical equivalence analysis examined ten candidates with one refutable match; the empirical hypothesis testing similarly found one refutable candidate among ten; and the generation-verification gap hypothesis also identified one overlapping work from ten examined. These statistics indicate that within the limited search scope, each core claim encounters some prior coverage, though nine out of ten candidates per contribution remain non-refutable or unclear. The modest search scale (thirty total candidates, not hundreds) means these findings reflect top semantic matches rather than exhaustive field coverage.

Given the limited literature search and the paper's position as the sole occupant of its taxonomy leaf, the work appears to address a relatively sparse research direction within a broader active field. The contribution-level statistics suggest that while individual claims may have partial precedents among closely related work, the integrated information-theoretic perspective on RL fine-tuning mechanisms occupies a distinct niche. The analysis is constrained by examining only top-thirty semantic matches, leaving open whether deeper literature exploration would reveal additional overlapping work or confirm the sparsity of this theoretical angle.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
3
Refutable Paper

Research Landscape Overview

Core task: understanding the value of reinforcement learning in foundation model fine-tuning. The field has organized itself around several complementary perspectives. Theoretical Foundations and Comparative Analysis examines why RL works for fine-tuning through information-theoretic lenses and first-principles reasoning, as exemplified by Roads to Likelihood[0]. Algorithmic Innovations explores novel training procedures—ranging from rejection sampling methods like Raft[1] to multi-agent frameworks such as MARFT[4]—that refine how RL updates are applied. Offline-to-Online RL and Pre-Training Integration investigates how pre-trained representations can be leveraged or adapted during RL fine-tuning, with works like Guiding Pretraining[3] and Offline RL Pretrained[23] bridging static datasets and interactive learning. Meta-Learning and Adaptation focuses on rapid task transfer and continual improvement, while Domain-Specific Applications demonstrate RL fine-tuning in robotics, medical reasoning, and recommendation systems. Data Efficiency and Prompt Optimization addresses sample complexity and the interplay between prompt engineering and RL-driven tuning. A particularly active contrast emerges between works that dissect RL's theoretical underpinnings versus those that engineer practical algorithms for specific modalities. Roads to Likelihood[0] sits squarely in the theoretical camp, offering an information-theoretic analysis of why RL fine-tuning improves likelihood-based objectives, closely aligned with Understanding RL Diffusion[5], which similarly unpacks RL's role in diffusion models from foundational principles. In contrast, nearby algorithmic studies like Reason RFT[2] and REFT[6] emphasize scalable training recipes for reasoning tasks, trading formal analysis for empirical gains. This tension—between understanding mechanisms and optimizing outcomes—runs throughout the taxonomy, with some branches prioritizing interpretability and others chasing state-of-the-art performance across diverse applications. The original paper's focus on information-theoretic clarity positions it as a bridge, helping practitioners understand not just how to apply RL fine-tuning, but why it succeeds where supervised methods plateau.

Claimed Contributions

Theoretical equivalence of online and offline preference fine-tuning under idealized assumptions

The authors prove that when using isomorphic function classes for policies and reward models, online reinforcement learning from human feedback (RLHF) and offline methods like DPO produce identical optimal policies. This result uses tools from information geometry to show that both approaches have the same set of optima regardless of preference dataset coverage.

10 retrieved papers
Can Refute
Empirical evidence against several hypotheses for the value of RL in preference fine-tuning

The authors conduct controlled experiments ruling out explanations based solely on better regularization to reference policies, computational benefits of on-policy sampling, or the ability to use wider data distributions for reward model training. They show these factors do not fully explain why online methods outperform offline methods.

10 retrieved papers
Can Refute
Generation-verification gap hypothesis explaining online fine-tuning advantages

The authors propose and provide evidence for the hypothesis that on problems where verifiers are simpler than generators, online fine-tuning reduces the policy search space to only those policies optimal for relatively simple verifiers. This proper learning approach requires less data than offline fine-tuning, which must search over all policies.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Theoretical equivalence of online and offline preference fine-tuning under idealized assumptions

The authors prove that when using isomorphic function classes for policies and reward models, online reinforcement learning from human feedback (RLHF) and offline methods like DPO produce identical optimal policies. This result uses tools from information geometry to show that both approaches have the same set of optima regardless of preference dataset coverage.

Contribution

Empirical evidence against several hypotheses for the value of RL in preference fine-tuning

The authors conduct controlled experiments ruling out explanations based solely on better regularization to reference policies, computational benefits of on-policy sampling, or the ability to use wider data distributions for reward model training. They show these factors do not fully explain why online methods outperform offline methods.

Contribution

Generation-verification gap hypothesis explaining online fine-tuning advantages

The authors propose and provide evidence for the hypothesis that on problems where verifiers are simpler than generators, online fine-tuning reduces the policy search space to only those policies optimal for relatively simple verifiers. This proper learning approach requires less data than offline fine-tuning, which must search over all policies.

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