FINER-Tuning: Fine-Grained Model Adaptation
- FINER-Tuning is a paradigm that employs fine-grained, system-specific adaptation techniques to refine pre-trained models beyond coarse end-to-end tuning.
- It replaces broad adaptation signals with local cues such as targeted data selection, detailed reward shaping, and precise preference optimization.
- Applications range from enhancing accuracy in machine-learned interatomic potentials and Text-to-SQL systems to reducing multimodal hallucinations.
Searching arXiv for papers related to “FINER-Tuning” and closely related variants. “FINER-Tuning” is not a single standardized method but a family resemblance across several recent research programs in which adaptation is made more targeted, more structured, or more fine-grained than conventional end-to-end fine-tuning. Across domains, the term has been used for at least four distinct but conceptually related ideas: system-specific fine-tuning of foundation interatomic potentials to near-ab initio accuracy (Hänseroth et al., 7 Nov 2025), fine-grained reinforcement learning for Text-to-SQL in small LLMs (Hoang et al., 5 May 2026), preference optimization for multimodal hallucination under fine-grained negative queries (Xiao et al., 18 Mar 2026), and finer-grained search or adaptation procedures in optimization and representation learning (Patanshetti et al., 2021, Liu et al., 2023, Zhu et al., 2024). A unifying interpretation is that FINER-Tuning replaces coarse adaptation signals—such as zero-shot transfer, binary rewards, broad architectural heuristics, or fixed spectral priors—with mechanisms that are local to the target system, target structure, or target failure mode. This suggests that the term functions less as a single algorithmic label than as an emerging methodological pattern.
1. FINER-Tuning as a cross-domain paradigm
In one explicit formulation, FINER-Tuning is described as a general paradigm: start from a strong, broadly trained foundation model and refine it with a small amount of carefully targeted data so that it becomes system-specific and architecture-agnostic in its final accuracy (Hänseroth et al., 7 Nov 2025). In atomistic machine-learned interatomic potentials (MLIPs), this means beginning with foundation models trained on large heterogeneous DFT corpora and then adapting them using a small, system-specific AIMD dataset, after which different architectures converge to essentially the same level of accuracy (Hänseroth et al., 7 Nov 2025). In Hadoop and Spark auto-tuning, “finer tuning” denotes a structured multi-stage search that begins with coarse exploration and then refines around influential parameters (Patanshetti et al., 2021). In FINER-SQL, the term refers to fine-grained execution-aware reinforcement learning signals that replace sparse binary SQL correctness rewards with denser and more interpretable supervision (Hoang et al., 5 May 2026). In multimodal hallucination mitigation, FINER-Tuning refers to DPO on FINER-inspired preference tuples built from positive and fine-grained negative queries, explicitly teaching models to reject subtle contradictions rather than merely score outputs post hoc (Xiao et al., 18 Mar 2026).
These uses differ in mechanics, but they share several recurring motifs. First, they begin with a pretrained or otherwise strong base model. Second, they identify a mismatch between coarse adaptation and the target task: architecture-dependent deviations in MLIPs (Hänseroth et al., 7 Nov 2025), sparse reward collapse in Text-to-SQL (Hoang et al., 5 May 2026), hallucinations under subtle multimodal contradictions (Xiao et al., 18 Mar 2026), or inefficient search over large parameter spaces (Patanshetti et al., 2021). Third, they introduce a finer adaptation signal: system-specific AIMD frames, operation-level overlap, verified reasoning memories, preference tuples over positive and negative multimodal answers, or finer local search over influential parameters. This suggests that FINER-Tuning is best understood as a strategy for reducing the granularity gap between pretraining and deployment.
2. System-specific adaptation in machine-learned interatomic potentials
A concrete and highly developed instance appears in “Fine-Tuning Unifies Foundational Machine-learned Interatomic Potential Architectures at ab initio Accuracy” (Hänseroth et al., 7 Nov 2025). The study benchmarks five MLIP families—MACE, GRACE, SevenNet, MatterSim, and ORB—across seven chemically diverse compounds and shows that fine-tuning universally enhances force predictions by factors of 5–15 and improves energy accuracy by 2–4 orders of magnitude (Hänseroth et al., 7 Nov 2025). The models span equivariant and invariant as well as conservative and non-conservative architectures, yet after fine-tuning their architecture-dependent spread largely disappears (Hänseroth et al., 7 Nov 2025).
The paper formalizes an important sense in which FINER-Tuning is both a universal adapter and a unifier. Before tuning, architectural choices matter substantially: equivariant vs invariant and conservative vs non-conservative models show different force and energy errors, and in some cases qualitatively different physical predictions (Hänseroth et al., 7 Nov 2025). After tuning on the same target system, these differences largely vanish: force errors collapse to about $0.02$–$0.07$ eV/Å across all models and systems, whereas foundation models were in the range of about $0.15$–$0.45$ eV/Å (Hänseroth et al., 7 Nov 2025). The loss is built from system-specific DFT energies and forces, with force-to-energy weight ratios tuned per framework and system, learning rates in the range –, epoch counts of 200–2500, and 70–90% of about 2000 configurations used for training (Hänseroth et al., 7 Nov 2025).
The data construction is central. For each of seven systems, the authors use Born–Oppenheimer AIMD from CP2K with BLYP or PBE, GTH pseudopotentials, DZVP-MOLOPT basis sets, and Nosé–Hoover chains at temperatures from 300–600 K (Hänseroth et al., 7 Nov 2025). From each trajectory, 2000 configurations are sampled equidistantly, every 100th AIMD frame, and each configuration provides atomic positions, total energy, and forces (Hänseroth et al., 7 Nov 2025). The resulting tuned models match AIMD or DFT-derived observables over 2–10 ns molecular dynamics for diffusion, RDFs, bond-length distributions, free-energy profiles, and vacancy barriers (Hänseroth et al., 7 Nov 2025). The study also introduces the aMACEing Toolkit, described as providing a unified and reproducible interface for fine-tuning workflows across multiple MLIP frameworks (Hänseroth et al., 7 Nov 2025).
This atomistic example gives one of the clearest encyclopedic definitions of FINER-Tuning: modest, carefully sampled, system-specific data can lock several distinct foundation architectures to the same target manifold, making architecture secondary to adaptation (Hänseroth et al., 7 Nov 2025). A plausible implication is that, in some scientific settings, the decisive factor is no longer which foundation architecture is selected, but whether a target-specific fine-tuning stage is performed at all.
3. Fine-grained reward shaping in Text-to-SQL
A second usage appears in “FINER-SQL: Boosting Small LLMs for Text-to-SQL” (Hoang et al., 5 May 2026). Here FINER-Tuning denotes fine-grained, execution-aware reinforcement learning of small LLMs, motivated by the failure of sparse binary execution rewards when most generated SQLs are initially incorrect (Hoang et al., 5 May 2026). The framework is built on group relative policy optimization and replaces sparse supervision with four reward components: format reward, execution reward, atomic reward, and memory reward (Hoang et al., 5 May 2026).
The method is two-stage. First, teacher models such as DeepSeek-R1, GPT-4o, and Qwen-2.5-72B generate a reasoning bank in a constrained format, and small Qwen2.5-Coder models of 0.5B, 1.5B, and 3B parameters are supervised-fine-tuned on those traces (Hoang et al., 5 May 2026). Second, reinforcement learning samples groups of rollouts and scores each rollout using a composite reward
then applies GRPO without a critic (Hoang et al., 5 May 2026). The execution reward is ternary—0 for failure to execute, 1 for executable but wrong, 2 for exact result match—while the atomic reward measures operation-level overlap via Jaccard similarity over atomic SQL decompositions, and the memory reward compares reasoning embeddings against a vector database of verified traces using cosine similarity (Hoang et al., 5 May 2026).
The paper reports that on BIRD dev, FINER-SQL with a 3B model reaches 67.73% execution accuracy, and on Spider dev it reaches 85.0%, while inference latency is 5.57 s/sample on a single A5000 with about 10 GB VRAM (Hoang et al., 5 May 2026). It is further reported that removing the memory reward drops execution accuracy by about 2–3 points across sizes and increases syntax error rates, while removing the atomic reward produces a similar drop (Hoang et al., 5 May 2026). Memory analysis indicates that with memory reward, Self-BLEU across reasoning traces increases and the mean number of execution groups per query decreases, supporting what the paper terms semantic stability (Hoang et al., 5 May 2026).
This case makes the “finer” in FINER-Tuning literal at the reward level. Instead of rewarding only fully correct outputs, the method rewards partial structural correctness and semantically aligned reasoning even when the final SQL is still wrong (Hoang et al., 5 May 2026). That is a different mechanism from the MLIP case, but the conceptual pattern is similar: coarse adaptation fails because the feedback signal is too sparse, and finer-grained supervision restores learnability.
4. Preference optimization for multimodal hallucination
In “FINER: MLLMs Hallucinate under Fine-grained Negative Queries,” FINER-Tuning denotes a DPO-based preference-optimization recipe for multimodal LLMs confronted with fine-grained negative queries (Xiao et al., 18 Mar 2026). The central empirical finding is that MLLMs hallucinate when fine-grained mismatches co-occur with genuinely present elements in the image, and the paper introduces two benchmarks, FINER-CompreCap and FINER-DOCCI, to measure this under multi-object, multi-attribute, multi-relation, and “what” settings (Xiao et al., 18 Mar 2026).
The training data for FINER-Tuning are not the benchmark items themselves but FINER-inspired preference tuples constructed from Pixmo-caption long captions (Xiao et al., 18 Mar 2026). Positive phrases and minimally edited negative phrases are extracted or generated with Phi-4-14B, then composed into paired positive and negative queries with accepted and rejected responses (Xiao et al., 18 Mar 2026). The resulting training set is
and DPO is applied with $0.07$0 to maximize the policy’s preference for the accepted answer over the rejected one, relative to a frozen reference model (Xiao et al., 18 Mar 2026).
The paper evaluates four frontier MLLMs—LLaVA-NeXT-7B, Qwen2.5-VL-7B-Instruct, InternVL-3.5-8B, and InternVL-3.5-14B—using LoRA with rank 32 on $0.07$1 and $0.07$2, global batch size 64, learning rate $0.07$3, one epoch, and at most 40k, 120k, or 160k examples depending on model (Xiao et al., 18 Mar 2026). For InternVL-3.5-14B, FINER-Tuning improves FINER-CompreCap paired accuracy from 74.5 to 80.0 on Multi-obj, from 68.1 to 78.9 on Multi-attr, from 47.0 to 71.2 on Multi-rel, and from 21.8 to 30.1 on Wh; on FINER-DOCCI it improves Multi-obj from 58.6 to 65.9, Multi-attr from 55.9 to 65.0, Multi-rel from 41.4 to 57.0, and Wh from 15.6 to 23.0 (Xiao et al., 18 Mar 2026). The paper also reports gains on eight other hallucination benchmarks and mild improvements on six general multimodal benchmarks, explicitly stating that FINER-Tuning does not impose an alignment tax (Xiao et al., 18 Mar 2026).
This version of FINER-Tuning is notable for being input-side and contradiction-aware. The model is not only taught to improve its answers; it is taught to detect when the query itself embeds a subtle false premise (Xiao et al., 18 Mar 2026). A plausible implication is that FINER-Tuning, in this multimodal sense, is a method for hardening models against semantically plausible but locally inconsistent prompts, rather than merely reducing output-level hallucination.
5. Related uses of “finer” or “fine-grained” tuning outside those core formulations
A broader encyclopedia treatment must distinguish FINER-Tuning from several neighboring usages. In “Auto Tuning of Hadoop and Spark parameters,” the phrase “Grid Search with Finer Tuning” describes a two-stage optimization procedure rather than model adaptation in the modern foundation-model sense (Patanshetti et al., 2021). The algorithm first performs coarse grid search over selected influential parameters, then narrows the search around the best coarse values for a small subset of most influential parameters (Patanshetti et al., 2021). On a 1 GB WordCount benchmark, the paper reports execution-time reductions of about 70.8% for Hadoop and 81.19% for Spark with Grid Search with Finer Tuning, compared with defaults (Patanshetti et al., 2021). The conceptual overlap is the coarse-to-fine search pattern, but the object being tuned is a system configuration rather than a pretrained model.
In implicit neural representations, “FINER” and “FINER++” refer to flexible spectral-bias tuning through variable-periodic activation functions rather than downstream parameter adaptation (Liu et al., 2023, Zhu et al., 2024). FINER introduces the activation
$0.07$4
and argues that by initializing biases within different ranges, sub-functions with various frequencies are selected, expanding the supported frequency set relative to SIREN (Liu et al., 2023). FINER++ generalizes this to variable-periodic versions of sine, Gaussian, and wavelet activations, with bias-controlled selection of sub-functions and scale-invariant frequency support tuning (Zhu et al., 2024). These papers are directly relevant to the semantic history of “FINER,” but they concern spectral-bias control in INRs, not post-pretraining adaptation.
Several additional works are conceptually adjacent. “BERTer: The Efficient One” treats finer-tuning as a layered collection of SMART regularization, hyperparameter tuning, cross-embedding architectures, and early exiting for BERT (Saligram et al., 2024). “Fine-tuning Done Right in Model Editing” argues that the failure of fine-tuning in editing was due to a depth-first, sample-wise pipeline rather than to fine-tuning itself, and introduces LocFT-BF based on breadth-first mini-batch optimization with localized tuning (Yang et al., 26 Sep 2025). “Fine-Tuning is Fine, if Calibrated” shows that fine-tuning on a subset of classes often preserves or improves absent-class representations, and that much of the apparent forgetting comes from discrepant logit scales recoverable via a simple post-hoc calibration bias $0.07$5 on absent-class logits (Mai et al., 2024). “Fisher-Guided Progressive Parameter Selection for Adaptive Fine-Tuning” introduces FisherAdapTune, which progressively freezes parameter groups whose Fisher structural drift stabilizes, using a scale-invariant Jensen–Shannon distance between Fisher distributions (Rostami et al., 8 Jun 2026). These works do not use the term FINER-Tuning in the same way, but they reinforce its broader association with more selective, more stable, or more diagnostically informed adaptation.
6. Common principles, misconceptions, and limitations
Across these papers, several common principles recur. One is that coarse adaptation often fails for reasons that are more specific than “fine-tuning does not work.” In MLIPs, zero-shot foundation models are robust but architecture-dependent, and system-specific fine-tuning removes those deviations (Hänseroth et al., 7 Nov 2025). In Text-to-SQL, sparse binary rewards collapse learning for small models, but dense fine-grained execution feedback stabilizes RL (Hoang et al., 5 May 2026). In multimodal reasoning, broad hallucination benchmarks miss the subtle false-premise regime, and FINER-Tuning directly targets that regime with preference data (Xiao et al., 18 Mar 2026). In model editing, the paper’s explicit claim is that the apparent failure of fine-tuning came from a depth-first editing pipeline and suboptimal tuning locations, not from an inherent limitation of fine-tuning itself (Yang et al., 26 Sep 2025). In classification under partial-class fine-tuning, the major issue can be group-wise logit calibration rather than representational forgetting (Mai et al., 2024).
A second common principle is that finer supervision often substitutes for larger scale. The MLIP case uses about 2000 configurations per system rather than retraining on massive corpora (Hänseroth et al., 7 Nov 2025). FINER-SQL uses dense reward shaping to make 0.5B–3B models competitive with much larger systems (Hoang et al., 5 May 2026). FINER-Tuning for multimodal hallucination improves 7B–14B MLLMs using 40k–160k preference tuples and single-pass DPO rather than multi-round RLHF (Xiao et al., 18 Mar 2026). A plausible implication is that FINER-Tuning often functions as a way of converting high-quality, task-local information into disproportionate gains, thereby partially substituting for brute-force scale.
The principal limitations are domain-specific. The MLIP study covers only seven systems, and its fine-tuned models are not systematically tested under large extrapolations in temperature, phase, or composition (Hänseroth et al., 7 Nov 2025). FINER-SQL still depends on execution engines, AST-based atomic decomposition, and high-quality verified traces, while highly complex queries remain difficult (Hoang et al., 5 May 2026). Multimodal FINER-Tuning depends on long captions and generated negative phrases, and Wh performance remains far below human levels even after tuning (Xiao et al., 18 Mar 2026). The Hadoop and Spark work uses execution time as a single metric and single-run measurements, leaving multi-objective and variance-aware tuning open (Patanshetti et al., 2021). The INR papers tune spectral support by initialization and activation design, but do not make the frequency-control mechanism adaptive during training (Liu et al., 2023, Zhu et al., 2024).
These limitations help avoid a common misconception: FINER-Tuning is not a universal guarantee that a small amount of fine-grained supervision will solve any adaptation problem. Rather, the papers consistently show that finer signals help when they are well matched to the structure of the failure mode—system-specific AIMD for MLIPs (Hänseroth et al., 7 Nov 2025), operation-level SQL overlap for Text-to-SQL (Hoang et al., 5 May 2026), or fine-grained negative multimodal queries for hallucination (Xiao et al., 18 Mar 2026).
7. Historical significance and emerging interpretation
The current literature suggests that “FINER-Tuning” is evolving into a descriptive label for adaptation strategies that improve on naïve fine-tuning by making the adaptation target more local, more structured, or more diagnostically grounded. Its historical development is distributed rather than canonical. Early uses of “finer tuning” in systems optimization emphasize hierarchical refinement over influential variables (Patanshetti et al., 2021). FINER and FINER++ in INRs stress flexible control over representational frequency support via activation design (Liu et al., 2023, Zhu et al., 2024). More recent foundation-model usages shift the emphasis toward structured post-pretraining adaptation: system-specific scientific tuning (Hänseroth et al., 7 Nov 2025), fine-grained RL for structured generation (Hoang et al., 5 May 2026), and preference-based correction of multimodal false-premise failures (Xiao et al., 18 Mar 2026).
Taken together, these works support an emerging interpretation of FINER-Tuning as a methodological family defined by three features. First, it starts from a capable base model rather than learning from scratch. Second, it identifies a mismatch that coarse adaptation leaves unresolved. Third, it introduces a finer adaptation mechanism—data selection, reward shaping, preference construction, parameter selection, or local search—chosen to align with the target structure of the problem. This suggests that FINER-Tuning is best understood not as a single recipe but as a research direction: a move away from undifferentiated full-model adaptation toward adaptation that is precise about what should change, why it should change, and at what granularity that change should be supervised.