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NeurIPS 2026

SemISP: Semantic-Consistent Diffusion for Cross-Camera RAW-to-sRGB Generation

ZHE PANG, Haina Qin, Ziyu Feng, Zewen Chen, Juan Wang, Bing Li, Weiming Hu

Cross-camera RAW-to-sRGB mapping seeks to convert smartphone RAW captures into DSLR-quality sRGB images, yet the large domain gap between linear RAW and nonlinear sRGB spaces makes this task highly challenging. Existing methods mainly condition on pixel-level supervision and local RAW cues, where content preservation and domain translation remain entangled in low-level measurements. We propose SemISP, a semantic-guided diffusion framework that uses scene semantics as a domain-invariant anchor for cross-domain generation. Specifically, we adopt a frozen DINOv3 as the sRGB semantic backbone and develop a Domain Alignment Adapter to extract sRGB-consistent semantic representations from RAW inputs through parameter-efficient adaptation. Because semantic priors and RAW signals operate at fundamentally different information levels, and the diffusion denoising process follows a coarse-to-fine trajectory, we further design a Time-aware Dual-stream Adaptive Modulation (TDAM) module that separately encodes both condition streams and uses the diffusion timestep to dynamically balance their contributions---emphasizing semantic anchoring at early stages and fine-grained RAW details at later ones. Experiments on two established cross-camera benchmarks show consistent improvements over prior methods in both fidelity and perceptual quality.

NeurIPS 2026

SemGeo-Gen: Unsupervised Generation of Approximate Cross-Instance Semantic-Geometric Correspondences

Roy Amoyal, Shira Ifergane, Oren Freifeld

We introduce SemGeo-Gen, an unsupervised method for generating approximate cross-instance semantic-geometric supervision from weakly aligned 3D object collections. Our goal is not to recover exact point-level correspondences, which are often ambiguous across different instances and would require expensive, impractical manual supervision at scale. Instead, we automatically produce approximate correspondences that are semantically meaningful, geometrically consistent, and diverse enough to train modern correspondence models. Given only coarse category-level rotation alignment, SemGeo-Gen lifts multi-view DINOv2 features to 3D, aligns object instances using continuous piecewise-affine registration, and prunes candidate matches using semantic consistency. The resulting 3D correspondences can be projected into rendered views, yielding scalable 3D-3D, 3D-2D, and 2D-2D supervision without manual annotation. We validate the generated correspondences against sparse human annotations from KeypointNet, obtaining 93% PCK@0.10 and 97% PCK@0.15. More importantly, we show that using the generated correspondences for synthetic pretraining improves recent state-of-the-art semantic correspondence models on SPair-71k after fine-tuning on real data, with gains of up to 5 PCK@0.10 points. Together, these results indicate that approximate semantic-geometric supervision generated from 3D assets can improve real-image correspondence learning and serve as a scalable alternative to costly manual annotation. Code and generated data will be released upon acceptance.

NeurIPS 2026

Selling Information While Being an Interested Party

Francesco Bacchiocchi, Matteo Castiglioni, Alberto Marchesi, Giulia Romano, Nicola Gatti

We study the algorithmic problem faced by an information holder (seller) who wants to optimally sell information to a budged-constrained decision maker (buyer). The utilities of both agents depend on a random state of nature that is revealed to the seller, but unknown to the buyer. Differently from previous works, we consider the case in which the seller is an interested party, as the decision taken by the buyer also influences seller's utility. The seller's goal is to (partially) sell their information about the state of nature to the buyer, so as to concurrently maximize revenue and induce the buyer to take a desirable decision. We study settings in which buyer's budget and utilities are determined by a random buyer's type unknown to the seller. First, we propose a polynomial-time algorithm for computing an optimal seller's protocol, which proposes a menu of information-revelation policies to the buyer, who acquires one of them by paying its corresponding price. Then, we switch the attention to the case in which the seller can only employ a single information-revelation policy, rather than proposing a menu. In such a setting, we completely characterize the computational complexity of the seller's algorithmic problem.

NeurIPS 2026

SelfGuard: Self-Supervised Deviation Modeling for Multi-Modal Jailbreak Detection

Chenchen Jing, Hongli Guo, Huarong Jia, TianweiBai, Che Sun, Yang Liu, Chunhua Shen

Multi-modal large language models (MLLMs) achieve strong vision–language reasoning ability but remain vulnerable to jailbreak attacks that exploit subtle cross-modal cues to bypass safety mechanisms. Detecting such attacks is difficult because harmful data are scarce and rapidly evolving, while detectors trained on known patterns often fail to generalize. In this paper, we cast multi-modal jailbreak detection as self-supervised deviation modeling and learn attack-agnostic signals from benign data only. We propose SelfGuard, a self-supervised framework, which models benign multi-modal regularities and learns to score structured violations via controlled pseudo-harmful deviations. SelfGuard synthesizes pseudo-harmful samples via controlled transformations that target three signature cues, cross-modal semantic inconsistency, format manipulation, and semantic toxicity. It then learns complementary deviation statistics through multi-task self-supervised learning, including misalignment discrimination, reconstruction-based toxicity modeling, and contrastive format modeling. At inference, SelfGuard performs multi-view deviation estimation by aggregating task-specific deviation scores to identify departures from benign multi-modal regularities. Extensive experiments on various benchmarks show the effectiveness of our method.

NeurIPS 2026

SelfCritic-VLA: Language as Intrinsic Critic for Vision Language Action Models in Autonomous Driving

Fang Li, Zheng Liu, Yuechen Luo, Hanbing Li, Zhi-Xin Yang

Vision-Language-Action (VLA) models offer a promising paradigm for end-to-end autonomous driving by connecting visual scene understanding, linguistic reasoning, and low-level trajectory generation. However, existing driving VLAs typically use language only as static commands or supervised rationales, while treating trajectory prediction as an open-loop generation problem. As a result, they lack an explicit mechanism for understanding the consequences of their own actions, which limits robustness under closed-loop distribution shift and makes reinforcement learning inefficient in long-tail scenarios. We propose SelfCritic-VLA, a self-evaluative VLA framework that unifies trajectory generation, language-grounded action critique, and trajectory refinement. Given a driving scene, the model first generates an initial trajectory, then produces a language-grounded critique that describes the potential safety, progress, and rule-compliance consequences of executing that trajectory, and finally refines the trajectory conditioned on this critique. To train this capability, we construct a large-scale counterfactual trajectory dataset with multi-source candidate trajectories and semantic critiques, and perform hybrid supervised fine-tuning for planning, action evaluation, and refinement. Building on this initialization, we introduce a self-critic reinforcement learning procedure based on GRPO, where critique-conditioned refinements are filtered by grounded driving rewards and distilled back into the base policy. Experiments on NAVSIM and Bench2Drive show that SelfCritic-VLA consistently improves both open-loop and closed-loop driving performance over strong baselines, demonstrating the effectiveness of language-guided self-refinement for driving policy optimization.

NeurIPS 2026

Self-Tuning Graph Filters via State-Dependent Operator Composition

Amir Ghazizadeh, Mahyar Alinejad, George Atia, Rickard Ewetz, Hao ZHENG

Most graph neural networks propagate information through a fixed-coefficient polynomial filter applied uniformly across every node and every layer. However, the appropriate filter varies along two distinct axes that such a parameterization cannot accommodate. Within a single graph, different nodes benefit from different mixtures of low-pass smoothing, high-pass contrast, and pass-through behavior. Across graphs, signal propagation dynamics themselves differ, with some graphs benefiting from long-range propagation across many hops while others require damping to prevent over-smoothing. The appropriate filter is therefore not a single fixed object at all, but a state-dependent composition whose behavior changes according to a node's current representation and its propagation history. We propose Castor, a self-tuning graph filter that realizes this composition through state-dependent decisions made at every step of propagation. A router reads each node's current state together with its initial state, and emits per-node mixing weights over three primitives, namely low-pass smoothing, high-pass contrast, and identity. A learnable per-hop coefficient then determines how much of the most recent change in the propagation is carried forward, accelerating the iteration when positive and damping it when negative. Setting the coefficient to zero recovers standard first-order propagation. Across fourteen standard node-classification benchmarks spanning the full homophily spectrum, a single Castor instance ranks top-three on every dataset and achieves an average rank of $2.07$, more than $3.5$ ranks ahead of the next-best of fourteen baselines.

NeurIPS 2026

Self-Trained Verification for Training- and Test-Time Self-Improvement

Chen Wu, Aditi Raghunathan

Self-improvement at scale has been a longstanding goal for reasoning models, and there are two natural places to do it: at test time, through verification-refinement (V-R) loops; and at training time, through self-training methods. Both are gated by the same bottleneck: the verifier. V-R loops stall when verifier scores inflate over rounds while accuracy stagnates, and when feedback is too generic to act on; self-training fails similarly when bad self-generated data are added to training. Better verifiers would unlock both, but the capability we want to train, i.e., catching errors the generator cannot detect in its own work, has neither finetuning data nor a verifiable reward. To address this challenge, we propose *self-trained verification* (STV). Our key observation is that, while a model cannot catch these errors alone, it can when shown the reference solution. We turn this asymmetry into a supervision target and train the verifier to imitate a more informed version of itself. At test time, STV substantially improves V-R loops on hard problems, while standard alternatives (e.g., SFT, RL on verifier scores, and even meta-verifiers) do not. STV roughly doubles accuracy on hard DAPO math and lifts it 14x on the hardest SciKnowEval problems (1.5% to 21%). At training time, starting from an RL-converged generator, putting the STV verifier in the loop yields a further 33% relative gain in test-time pass@1. More notably, the generator's standalone pass@1, with no verifier at inference time, climbs 30% relative past where continued RL had converged. Hence, the next frontier in reasoning may lie in how we train verifiers.

NeurIPS 2026

Self-Supervised On-Policy Reinforcement Learning via Contrastive Proximal Policy Optimisation

Asim Osman, Sasha Abramowitz, Mark Bergh, Ulrich Armel Mbou Sob, Ruan John de Kock, Omayma Mahjoub, Oussama Hidaoui, Noah De Nicola, Arnol M Fokam, Felix Chalumeau, Daniel Rajaonarivonivelomanantsoa, Siddarth Singh, Refiloe Shabe, Juan Formanek, Simon Du Toit, Arnu Pretorius

Contrastive reinforcement learning (CRL) learns goal-conditioned Q-values through a contrastive objective over state-action and goal representations, removing the need for hand-crafted reward functions. Despite impressive success in achieving viable self-supervised learning in RL, all existing CRL algorithms rely on off-policy optimisation and are mostly constrained to continuous action spaces, with little research invested in discrete environments. This leaves CRL disconnected from widely used and effective, modern on-policy training pipelines adopted across both single-agent and multi-agent RL in continuous and discrete environments. To establish a first connection, we introduce Contrastive Proximal Policy Optimisation (CPPO). CPPO is an on-policy contrastive RL algorithm that derives policy advantages directly from contrastive Q-values and optimises them via the standard PPO objective, without requiring a reward function or a replay buffer. We evaluate CPPO across continuous and discrete, single-agent and cooperative multi-agent tasks. Whilst the existence of an on-policy approach is inherently useful, we additionally observe that CPPO not only significantly outperforms the previous CRL baselines in 14 out of the 18 tasks benchmarked, but matches or exceeds reward-based PPO's performance in 12 out of the 18 tasks without any reward signal.

NeurIPS 2026

Self-Supervised Keyframe Discovery for Horizon-Invariant Behavior Cloning

Prabin Kumar Rath, Omkar Patil, Nakul Gopalan

Behavior cloning (BC) in non-Markovian environments is a challenging problem because policies have to reason over contextual information over long horizons. Existing policy architectures rely on recurrent or attention-based mechanisms to capture long-term dependencies. However, recurrent models suffer from hidden-state collapse and gradient instability under backpropagation through time, while attention-based models are fundamentally limited by context length. To address these issues, we propose Keyframe Mnemonics, a novel self-supervised method that $\textit{discovers}$ a set of information-critical observations ($\textit{mnemonics}$) by learning an objective from randomly sampled past observations and using it as a reward for keyframe selection. We then train a BC policy that conditions on the discovered keyframes to model the action distribution. Under near-optimal keyframe selection, our formulation provides context retention guarantees over an infinite horizon, while maintaining a small set of decision-relevant keyframes in the policy's working memory. We evaluate our method on synthetic memory domains, where mnemonic-conditioned BC policies achieve $100$% success rates (SR) and generalize to horizons orders of magnitude beyond training without performance degradation. Additionally, we evaluate on memory intensive robot manipulation benchmarks, where we observe an absolute $15$-$30$% improvement in SR over strong memory-augmented baselines on $23$ tasks. Code and videos are available at https://keyframe-mnemonics.github.io.

NeurIPS 2026

Self-Supervised Goal-Reaching Results in Multi-Agent Cooperation and Exploration

Chirayu Nimonkar, Shlok Shah, Catherine Ji, Benjamin Eysenbach

For groups of autonomous agents to achieve a particular goal, they must engage in coordination and long-horizon reasoning. Rather than relying on complex reward functions and explicit cooperation mechanisms, we ask what minimal ingredients are required for effective coordination and exploration to emerge in multi-agent settings. We investigate this question through self-supervised goal-reaching, where agents aim to maximize the likelihood of visiting a goal state rather than maximizing a reward. Despite a sparse feedback signal, we present empirical results that show self-supervised goal-reaching techniques enable agents to learn from such feedback. On MARL benchmarks, self-supervised goal-reaching outperforms alternative approaches that have access to the same sparse reward signal. Furthermore, we empirically demonstrate that multi-agent self-supervised goal-reaching approaches can be more robust than single-agent strategies. While there is no *explicit* exploration mechanism, this approach explores nontrivial intermediate coordination strategies in sparse settings where alternative approaches fail to achieve a single success.

NeurIPS 2026

Self-Supervised Doppler-Guided RF Odometry

Kunzhe Song, Jialuo Du, Jingkai Lin, Huacheng Zeng

Accurate RF odometry remains difficult due to multipath interference, geometric degeneracy, and dynamic clutter. Existing approaches mitigate these challenges by relying on auxiliary sensors or ground-truth pose supervision, which limits scalability and deployability. We present RFWalk, a self-supervised, RF-only odometry framework that learns cross-frame correspondence without any pose annotations or auxiliary sensors. Our key insight is to cast each range-azimuth cell as a node in a space-time graph and recover inter-frame motion by learning a soft transition matrix through cycle-consistent contrastive random walks. To resolve geometric degeneracy in feature-poor scenes, we further introduce a physics-informed Doppler consistency loss that enforces agreement between the correspondence-induced displacement field and measured radial velocities. Experiments on challenging scenes show that RFWalk achieves competitive accuracy in static environments and substantially outperforms existing approaches in dynamic scenes.

NeurIPS 2026

Self-Rewarded Multimodal Coherent Reasoning Across Diverse Visual Domains

Jusheng zhang, Ningyuan Liu, Kaitong Cai, Sidi Liu, Ziliang Chen, Wenhao Wang, Keze Wang

Multimodal LLMs often produce fluent yet unreliable reasoning, exhibiting weak step-to-step coherence and insufficient visual grounding, largely because existing alignment approaches supervise only the final answer while ignoring the reliability of the intermediate reasoning process. We introduce SR-MCR, a lightweight and label-free framework that aligns reasoning by exploiting intrinsic process signals derived directly from model outputs. Five self-referential cues—semantic alignment, lexical fidelity, non-redundancy, visual grounding, and step consistency—are integrated into a normalized, reliability-weighted reward that provides fine-grained process-level guidance. A critic-free GRPO objective, enhanced with a confidence-aware cooling mechanism, further stabilizes training and suppresses trivial or overly confident generations. Built on Qwen2.5-VL, SR-MCR improves both answer accuracy and reasoning coherence across a broad set of visual benchmarks; among open-source models of comparable size, SR-MCR-7B achieves state-of-the-art performance with an average accuracy of 81.4%. Ablation studies confirm the independent contributions of each reward term and the cooling module.

NeurIPS 2026

Self-Recognition Finetuning can Reverse and Prevent Emergent Misalignment

Arush Tagade, Shaoheng Zhou, Jiaxin Wen, Shi Feng

Emergent misalignment (EM) has been linked to the activation of misaligned persona vectors and evil character traits, suggesting that EM operates through disruption of the model's aligned character rather than direct learning of harmful content. Motivated by this connection, we study self-generated text recognition (SGTR) finetuning as a character-targeted intervention that is distinct from existing in-training defenses. We conduct two-stage finetuning experiments across three models (GPT-4.1, Qwen2.5-32B-Instruct, Seed-OSS-36B-Instruct) and multiple EM datasets to compare SGTR finetuning against benign finetuning baselines (correct domain-specific data, general knowledge, and word counting) to find it an effective defense in both reversal and prevention settings. We find that all interventions produce comparable EM reversal, but only when restoring capabilities that EM had degraded. For prevention, only SGTR finetuning consistently reduces misalignment without exacerbating any individual metric, suggesting that character fortification specifically drives prevention. We provide further evidence for EM's relation to the LLM's default character by showing that EM finetuning induces diversity into the LLM's identity self-reports, artificially corrupting self-recognition exacerbates misalignment caused by EM finetuning, and that removing the model's identity-bearing system prompt substantially reduces the effect of EM finetuning. Together, these findings reframe EM not as the adoption of a coherent misaligned persona but as the destabilization of aligned character.

NeurIPS 2026

Self-Programmed Execution for Language-Model Agents

Luke J O&#x27, Connor

At the heart of existing language model agents is a fixed orchestrator program which is responsible for the state transition between consecutive turns. This paper introduces \emph{self-programmed execution} (SPE), an agent architecture in which the model completion is itself the orchestrator program, and the harness evaluates this program but does not impose its own orchestration policy. I formalize this idea using agentic machines: an SPE state is one from which a model completion can load any state of an embedded copy of the machine, meaning that it is subject to no fixed turn-to-turn orchestration policy. Realizing SPE in practice is nontrivial because the same data is both model context and executable program. I therefore introduce \textsc{Spell}, a Lisp-based language in which programs can edit and re-evaluate themselves, and effectful expressions like model invocations are structured such that re-evaluating an edited program does not replay its side effects. Experiments with existing models, not trained for SPE or \textsc{Spell}, show that frontier models can operate in this regime and accomplish challenging agentic tasks. These results demonstrate how an LM can act as an agent without any fixed orchestration policy, and they raise the question of what self-orchestration strategies might be learned by a model trained for self-programmed execution.

NeurIPS 2026

Self-Improvement Imitation with Biologically Guided Search for Protein Design Under Oracle Budgets

Ashima Khanna-Reiter, Dominik G Grimm

Protein sequence optimization under tight oracle budgets requires methods that explore vast combinatorial spaces while making each evaluation informative. Existing reinforcement learning and off-policy generative approaches often degrade under surrogate noise, and position-agnostic mutation proposals risk disrupting functionally critical residues. We introduce SILO, a trajectory-level self-improvement imitation framework for oracle-budgeted protein design. SILO uses a hierarchical edit policy that decomposes each mutation into a position choice followed by a residue choice. In each active-learning round, the policy samples candidate trajectories via incremental stochastic beam search without replacement (SBS), and a UCB-based proxy ensemble, combined with an alanine-scan fitness score (AFS), selects candidates with functionally relevant edits for in silico oracle evaluation. The policy is then updated by next-action cross-entropy imitation on the round’s best oracle-labeled trajectories, avoiding value-function estimation. Across eight reproduced protein fitness landscapes and five strong baselines from prior work, SILO achieves the highest maximum and top-100 mean fitness on 8 of 8 landscapes within our evaluations, often exhibiting faster early-stage improvement. In low-data and noisy-proxy stress tests on two landscapes per setting, SILO remains competitive or best when several baselines degrade. Ablations show that SBS with AFS account for much of the gains, with iterative imitation providing additional improvement.

NeurIPS 2026

Self-Evolving Agents Should Build Internal and External Models of the World

Luiz Felipe Vecchietti, Bryan Nathanael Wijaya, Wenchao Dong, Meeyoung Cha

Developing self-evolving agents that learn continuously from streams of information represents an emerging frontier in artificial intelligence. This capability is critical for efficiently updating computationally expensive systems such as foundation models. When agents learn by exploring the environment and interacting with other agents to achieve multiple tasks over time, two sources of non-stationarity emerge: shifting task distributions and co-evolving peers. However, developing frameworks for continual multi-agent reinforcement learning (CMARL) and effective policy improvement remains challenging. In this paper, we argue that training self-evolving agents should incorporate two classes of model-based principles: internal models that allow an agent to compare policy checkpoints, monitor its learning capacity, and represent its current objective; and external models that learn environment dynamics and the behavior of other agents. We anticipate that discussion on these principles will facilitate the design of algorithms, architectures, and benchmarks that explicitly evaluate model-based components in CMARL.

NeurIPS 2026

Self-EvolveRec: Self-Evolving Recommender Systems with LLM-based Directional Feedback

Sein Kim, Sangwu Park, HongSeok Kang, Wonjoong Kim, Jimin Seo, Yeonjun In, Kanghoon Yoon, Hyunsik Jeon, Chanyoung Park

Traditional methods for automating recommender system design, such as Neural Architecture Search (NAS), are often constrained by a fixed search space defined by human priors, limiting innovation to pre-defined operators. While recent LLM-driven code evolution frameworks shift fixed search space target to open-ended program spaces, they primarily rely on scalar metrics (e.g., NDCG, Hit Ratio) that fail to provide qualitative insights into model failures or directional guidance for improvement. To address this, we propose Self-EvolveRec, a novel framework that establishes a directional feedback loop by integrating a User Simulator for qualitative critiques and a Model Diagnosis Tool for quantitative internal verification. Furthermore, we introduce a Diagnosis Tool - Model Co-Evolution strategy to ensure that evaluation criteria dynamically adapt as the recommendation architecture evolves. Extensive experiments demonstrate that Self-EvolveRec significantly outperforms state-of-the-art NAS and LLM-driven code evolution baselines in both recommendation performance and user satisfaction. Our code is available at https://anonymous.4open.science/r/selfevolverec-37F5/.

NeurIPS 2026

Self-Evolution Reasoner: Continuous Optimization via Policy-Intrinsic Exploration and Exploitation

Wenhang Shi, Yiren Chen, Zhe Zhao, Jinhao Dong, Wei Lu, Xiaoyong Du

Reinforcement learning with verifiable rewards (RLVR) enables large language models (LLMs) to continuously self-improve their reasoning capabilities. Compared to RLVR's scalar rewards, recent self-supervised on-policy distillation methods leverage denser supervision signals. However, both paradigms suffer from exploration bottlenecks, struggling to escape local optima when the policy fails to sample valid solutions. Relying on external demonstrations can bypass this issue; however, the off-policy nature hinders genuine self-improvement and the mismatched-distribution reasoning can not be fully exploited. In this work, we propose Self-Evolution Policy Optimization (SEPO), a framework that drives continuous reasoning capability evolution via guided exploration and in-distribution exploitation. Specifically, SEPO utilizes labels to elicit correct reasoning from the policy, unlocking the exploration potential. By treating current policy conditioned on this in-distribution reasoning as a self-teacher, SEPO distills the newly discovered solution back into the policy. Through this iterative cycle of policy-intrinsic exploration and exploitation, SEPO achieves robust and continuous capability evolution. Across scientific, tool-use, and complex logical reasoning domains, SEPO consistently achieves the best performance under identical training time or steps, demonstrating superior efficiency and convergence ceilings. Notably, SEPO maintains stable improvements even in scenarios where baselines severely underperform or fail entirely, such as on complex tasks or with small-scale models.

NeurIPS 2026

Self-Distilled RLVR

Chenxu Yang, Chuanyu Qin, Qingyi Si, Minghui Chen, Naibin Gu, Dingyu Yao, Zheng Lin, Weiping Wang, Nan Duan, Jiaqi Wang

On-policy distillation (OPD) has become a popular training paradigm in the LLM community. This paradigm selects a larger model as the teacher to provide dense, fine-grained signals for each sampled trajectory, in contrast to reinforcement learning with verifiable rewards (RLVR), which only obtains sparse signals from verifiable outcomes in the environment. Recently, the community has explored on-policy self-distillation (OPSD), where the same model serves as both teacher and student, with the teacher receiving additional privileged information such as reference answers to enable self-evolution. This paper demonstrates that learning signals solely derived from the privileged teacher result in severe information leakage and unstable long-term training. Accordingly, we identify the optimal niche for self-distillation and propose \textbf{RLSD} (\textbf{RL}VR with \textbf{S}elf-\textbf{D}istillation). Specifically, we leverage self-distillation to obtain token-level policy differences for determining fine-grained update magnitudes, while continuing to use RLVR to derive reliable update directions from environmental feedback (e.g., response correctness). This enables RLSD to simultaneously harness the strengths of both RLVR and OPSD, achieving a higher convergence ceiling and superior training stability.

NeurIPS 2026

Self-Distillation of Hidden Layers for Masked Self-Supervised Representation Learning

Scott C. Lowe, Anthony Fuller, Sageev Oore, Evan Shelhamer, Graham Taylor

The landscape of self-supervised learning (SSL) is currently dominated by generative approaches (e.g. MAE) that reconstruct raw low-level data, and predictive approaches (e.g. I-JEPA) that predict high-level abstract embeddings. While generative methods are stable due to their reliable training targets based on ground-truth data, they are computationally inefficient for high-redundancy modalities like imagery, and their training objective does not prioritize learning high-level, conceptual features. Conversely, predictive methods often suffer from training instability due to their reliance on the non-stationary targets of final-layer self-distillation. We introduce Bootleg, a method that bridges this divide by tasking the model with predicting latent representations from multiple hidden layers of a teacher. This hierarchical objective forces the model to capture features at varying levels of abstraction simultaneously. We demonstrate Bootleg significantly outperforms comparable baselines (+10% vs. I-JEPA) on frozen probe classification of ImageNet-1K, iNaturalist-21, and VTAB, and semantic segmentation of ADE20K, Cityscapes, and COCO-Stuff.

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