GenAI4ELab/papercli-papers huggingface dataset:
APC-RL: Exceeding data-driven behavior priors with adaptive policy composition
Authors: Finn Rietz; Pedro Zuidberg Dos Martires; Johannes A. Stork
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Incorporating demonstration data into reinforcement learning (RL) can greatly accelerate learning, but existing approaches often assume demonstrations are optimal and fully aligned with the target task.
In practice, demonstrations are frequently sparse, suboptimal, or misaligned, which can degrade performance when these demonstrations are integrated into RL.
We propose Adaptive Policy Composition (APC), a hierarchical model that adaptively composes multiple data-driven Normalizing Flow (NF) priors.
Instead of enforcing strict adherence to the priors, APC estimates each prior's applicability to the target task while leveraging them for exploration. 
Moreover, APC either refines useful priors, or sidesteps misaligned ones when necessary to optimize downstream reward.
Across diverse benchmarks, APC accelerates learning when demonstrations are aligned, remains robust under severe misalignment, and leverages suboptimal demonstrations to bootstrap exploration while avoiding performance ceilings caused by overly strict adherence to suboptimal demonstrations.
Fine-tuning Behavioral Cloning Policies with Preference‑Based Reinforcement Learning
Authors: Mael Macuglia; Paul Friedrich; Giorgia Ramponi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deploying reinforcement learning (RL) in robotics, industry, and health care is blocked by two obstacles: the difficulty of specifying accurate rewards and the risk of unsafe, data-hungry exploration. We address this by proposing a two-stage framework that first learns a safe initial policy from a reward-free dataset of expert demonstrations, then fine-tunes it online using preference-based human feedback. We provide the first principled analysis of this offline-to-online approach and introduce BRIDGE, a unified algorithm that integrates both signals via an uncertainty-weighted objective. We derive regret bounds that shrink with the number of offline demonstrations, explicitly connecting the quantity of offline data to online sample efficiency. We validate BRIDGE in discrete and continuous control MuJoCo environments, showing it achieves lower regret than both standalone behavioral cloning and online preference-based RL. Our work establishes a theoretical foundation for designing more sample-efficient interactive agents.
Generalization in LLM Problem Solving: The Case of the Shortest Path
Authors: Yao Tong; Jiayuan Ye; Anastasia Borovykh; Reza Shokri
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Whether language models can systematically generalize remains actively debated. 
Yet empirical performance is jointly shaped by multiple factors such as training data, training paradigms, and inference-time strategies, making failures difficult to interpret. We introduce a controlled synthetic environment based on shortest-path planning, a canonical composable sequential optimization problem. 
The setup enables clean separation of these factors and supports two orthogonal axes of generalization: ***spatial transfer*** to unseen maps and ***length scaling*** to longer-horizon problems.
We find that models exhibit strong spatial transfer but consistently fail under length scaling due to recursive instability.
We further analyze how distinct stages of the learning pipeline influence systematic problem-solving:
for example, data coverage sets capability limits; reinforcement learning improves training stability but does not expand those limits; and inference-time scaling enhances performance but cannot rescue length-scaling failures.
A New Initialization to Control Gradients in Sinusoidal Neural Networks
Authors: Andrea Combette; Nelly Pustelnik; Antoine Venaille
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Proper initialisation strategy is of primary importance to mitigate gradient explosion or vanishing when training neural networks. Yet, the impact of initialisation parameters still lacks a precise theoretical understanding for several well-established architectures. Here, we propose a new initialisation for networks with sinusoidal activation functions such as \texttt{SIREN}, focusing on gradients control, their scaling with network depth, their impact on training and on generalization. To achieve this, we identify a closed-form expression for the initialisation of the parameters, differing from the original \texttt{SIREN} scheme. This expression is derived from fixed points obtained through the convergence of pre-activation distribution and the variance of Jacobian sequences. Controlling both gradients  and targeting vanishing pre-activation helps preventing the emergence of inappropriate frequencies during estimation, thereby improving generalization. We further show that this initialisation strongly influences training dynamics through the Neural Tangent Kernel framework (NTK). Finally, we benchmark \texttt{SIREN} with the proposed initialisation against the original scheme and other baselines on function fitting and image reconstruction. The new initialisation consistently outperforms state-of-the-art methods across a wide range of reconstruction tasks, including those involving physics-informed neural networks.
From Large to Small: Transferring CUDA Optimization Expertise via Reasoning Graph
Authors: Junfeng Gong; Zhiyi Wei; Junying Chen; Cheng Liu; Huawei Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Despite significant evolution of CUDA programming and domain-specific libraries, effectively utilizing GPUs with massively parallel engines remains difficult. Large language models (LLMs) show strong potential in generating optimized CUDA code from sequential code. However, using LLMs in practice faces two major challenges: cloud-based APIs pose risks of code leakage, and local deployment is often computationally expensive and inefficient. These drawbacks have spurred interest in small language models (SLMs), which are more lightweight and privacy-friendly. Encouragingly, recent studies show that SLMs can achieve performance comparable to LLMs on specific tasks. While SLMs can match LLMs on domain-specific tasks, their limited reasoning abilities lead to suboptimal performance in complex CUDA generation according to our experiments.
To bridge this gap, we propose ReGraphT, a training-free, retrieval-augmented generation framework that transfers LLM-level reasoning to smaller models. ReGraphT organizes CUDA optimization trajectories into a structured reasoning graph, modeling the combined CUDA optimizations as state transitions, and leverages Monte Carlo Graph Search (MCGS) for efficient exploration.
We also present a CUDA-specific benchmark with difficulty tiers defined by reasoning complexity to evaluate models more comprehensively. Experiments show that ReGraphT outperforms HPC-specific fine-tuned models and other retrieval-augmented approaches, achieving an average 2.33× speedup on CUDAEval and ParEval. When paired with DeepSeek-Coder-V2-Lite-Instruct and Qwen2.5-Coder-7B-Instruct, ReGraphT enables SLMs to approach LLM-level performance without the associated privacy risks or excessive computing overhead.
A Schrödinger Eigenfunction Method for Long-Horizon Stochastic Optimal Control
Authors: Louis Claeys; Artur Goldman; Zebang Shen; Niao He
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: High-dimensional stochastic optimal control (SOC) becomes harder with longer planning horizons: existing methods scale linearly in the horizon $T$, with performance often deteriorating exponentially. We overcome these limitations for a subclass of linearly-solvable SOC problems—those whose uncontrolled drift is the gradient of a potential. In this setting, the Hamilton-Jacobi-Bellman equation reduces to a linear PDE governed by an operator $\mathcal{L}$. We prove that, under the gradient drift assumption, $\mathcal{L}$ is unitarily equivalent to a Schrödinger operator $\mathcal{S} = -\Delta + \mathcal{V}$ with purely discrete spectrum, allowing the long-horizon control to be efficiently described via the eigensystem of $\mathcal{L}$. This connection provides two key results: first, for a symmetric linear-quadratic regulator (LQR), $\mathcal{S}$ matches the Hamiltonian of a quantum harmonic oscillator, whose closed-form eigensystem yields an analytic solution to the symmetric LQR with arbitrary terminal cost. Second, in a more general setting, we learn the eigensystem of $\mathcal{L}$ using neural networks. We identify implicit reweighting issues with existing eigenfunction learning losses that degrade performance in control tasks, and propose a novel loss function to mitigate this. We evaluate our method on several long-horizon benchmarks, achieving an order-of-magnitude improvement in control accuracy compared to state-of-the-art methods, while reducing memory usage and runtime complexity from $\mathcal{O}(Td)$ to $\mathcal{O}(d)$.
Optimizing Agent Planning for Security and Autonomy
Authors: Aashish Kolluri; Rishi Sharma; Manuel Costa; Boris Köpf; Tobias Nießen; Mark Russinovich; Shruti Tople; Santiago Zanella-Beguelin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Indirect prompt injection attacks threaten AI agents that execute consequential actions, motivating deterministic system-level defenses. Such defenses can provably block unsafe actions by enforcing confidentiality and integrity policies, but currently appear costly: they reduce task completion rates and increase token usage compared to probabilistic defenses. We argue that existing evaluations miss a key benefit of system-level defenses: reduced reliance on human oversight. We introduce autonomy metrics to quantify this benefit: the fraction of consequential actions an agent can execute without human-in-the-loop (HITL) approval while preserving security. To increase autonomy, we design a security-aware agent that (i) introduces richer HITL interactions, and (ii) explicitly plans for both task progress and policy compliance. We implement this agent design atop an existing information-flow control defense against prompt injection and evaluate it on the AgentDojo and WASP benchmarks. Experiments show that this approach yields higher autonomy without sacrificing utility (task completion).
SteinsGate: Adding Causality to Diffusions for Long Video Generation via Path Integral
Authors: Yufei Huang; Liangyu Yuan; Changxi Chi; Yunfan Liu; Cheng Tan; Siyuan Li; Jingbo Zhou; Haitao Lin; Chang Yu; Stan Z. Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Video generation has advanced rapidly, but current models remain limited to short clips, far from the length and complexity of real-world narratives. Long video generation is thus both important and challenging. Existing approaches either attempt to extend the modeling length of video diffusion models directly or merge short clips via shared frames. However, due to the lack of temporal causality modeling for video data, they achieve only limited extensions, suffer from discontinuous or even contradictory actions, and fail to support flexible and fine-grained temporal control. Thus, we propose Instruct-Video-Continuation (InstructVC), combining Temporal Action Binding for fine-grained temporal control and Causal Video Continuation for natural long-term simulation. Temporal Action Binding decomposes complex long videos by temporal causality into scene descriptions and action sequences with predicted durations, while Causal Video Continuation autoregressively generates coherent video narratives from the text story. We further introduce SteinsGate, an inference-time instance of InstructVC that uses an MLLM for Temporal Action Binding and Video Path Integral to enforce causality between actions, converting a pre-trained TI2V diffusion model into an autoregressive video continuation model. Benchmark results demonstrate the advantages of SteinsGate and InstructVC in achieving accurate temporal control and generating natural, smooth multi-action long videos.
Fast training of accurate physics-informed neural networks without gradient descent
Authors: Chinmay Datar; Taniya Kapoor; Abhishek Chandra; Qing Sun; Erik Lien Bolager; Iryna Burak; Anna Veselovska; Massimo Fornasier; Felix Dietrich
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Solving time-dependent Partial Differential Equations (PDEs) is one of the most critical problems in computational science. While Physics-Informed Neural Networks (PINNs) offer a promising framework for approximating PDE solutions, their accuracy and training speed are limited by two core barriers: gradient-descent-based iterative optimization over complex loss landscapes and non-causal treatment of time as an extra spatial dimension. We present Frozen-PINN, a novel PINN based on the principle of space-time separation that leverages random features instead of training with gradient descent, and incorporates temporal causality by construction. On nine PDE benchmarks, including challenges like extreme advection speeds, shocks, and high-dimensionality, Frozen-PINNs achieve superior training efficiency and accuracy over state-of-the-art PINNs, often by several orders of magnitude. Our work addresses longstanding training and accuracy bottlenecks of PINNs, delivering quickly trainable, highly accurate, and inherently causal PDE solvers, a combination that prior methods could not realize. Our approach challenges the reliance of PINNs on stochastic gradient-descent-based methods and specialized hardware, leading to a paradigm shift in PINN training and providing a challenging benchmark for the community.
Cross-Embodiment Offline Reinforcement Learning for Heterogeneous Robot Datasets
Authors: Haruki Abe; Takayuki Osa; Yusuke Mukuta; Tatsuya Harada
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Scalable robot policy pre-training has been hindered by the high cost of collecting high-quality demonstrations for each platform. In this study, we address this issue by uniting offline reinforcement learning (offline RL) with cross-embodiment learning. Offline RL leverages both expert and abundant suboptimal data, and cross-embodiment learning aggregates heterogeneous robot trajectories across diverse morphologies to acquire universal control priors. We perform a systematic analysis of this offline RL and cross-embodiment paradigm, providing a principled understanding of its strengths and limitations. To evaluate this offline RL and cross-embodiment paradigm, we construct a suite of locomotion datasets spanning 16 distinct robot platforms. Our experiments confirm that this combined approach excels at pre-training with datasets rich in suboptimal trajectories, outperforming pure behavior cloning. However, as the proportion of suboptimal data and the number of robot types increase, we observe that conflicting gradients across morphologies begin to impede learning. To mitigate this, we introduce an embodiment-based grouping strategy in which robots are clustered by morphological similarity and the model is updated with a group gradient. This simple, static grouping substantially reduces inter-robot conflicts and outperforms existing conflict-resolution methods. Project page: https://haruki-abe.github.io/cross_embodiment_offline_rl_website
Experience-based Knowledge Correction for Robust Planning in Minecraft
Authors: Seungjoon Lee; Suhwan Kim; Minhyeon Oh; Youngsik Yoon; Jungseul Ok
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Model (LLM)-based planning has advanced embodied agents in long-horizon environments such as Minecraft, where acquiring latent knowledge of goal (or item) dependencies and feasible actions is critical. However, LLMs often begin with flawed priors and fail to correct them through prompting, even with feedback. We present XENON (eXpErience-based kNOwledge correctioN), an agent that algorithmically revises knowledge from experience, enabling robustness to flawed priors and sparse binary feedback. XENON integrates two mechanisms: Adaptive Dependency Graph, which corrects item dependencies using past successes, and Failure-aware Action Memory, which corrects action knowledge using past failures. Together, these components allow XENON to acquire complex dependencies despite limited guidance. Experiments across multiple Minecraft benchmarks show that XENON outperforms prior agents in both knowledge learning and long-horizon planning. Remarkably, with only a 7B open-weight LLM, XENON surpasses agents that rely on much larger proprietary models.
Computational Barriers to Filtering for AI Alignment
Authors: Sarah Ball; Grzegorz Gluch; Shafi Goldwasser; Frauke Kreuter; Omer Reingold; Guy N. Rothblum
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: With the increased deployment of large language models (LLMs), one concern is their potential misuse for generating harmful content. Our work studies the alignment challenge, with a focus on filters to prevent the generation of unsafe information. Two natural points of intervention are the filtering of the input prompt before it reaches the model, and filtering the output after generation. Our main results demonstrate computational challenges in filtering both prompts and outputs. First, we show that there exist LLMs for which there are no efficient input-prompt filters: adversarial prompts that elicit harmful behavior can be easily constructed, which are computationally indistinguishable from benign prompts for any efficient filter. Our second main result identifies a natural setting in which output filtering is computationally intractable. All of our separation results are under cryptographic hardness assumptions. In addition to these core findings, we also formalize and study relaxed mitigation approaches, demonstrating further computational barriers. We conclude that safety cannot be achieved by designing filters external to the LLM internals (architecture and weights); in particular, black-box access to the LLM will not suffice. Based on our technical results, we argue that an aligned AI system’s intelligence cannot be separated from its judgment.
Inoculation Prompting: Eliciting traits from LLMs during training can reduce trait expression at test-time
Authors: Daniel Tan; Anders Cairns Woodruff; Niels Warncke; Arun Jose; Maxime Nicolas Riché; David Demitri Africa; Mia Taylor
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Language model finetuning often results in learning undesirable traits in combination with desired ones. To address this, we propose inoculation prompting: modifying finetuning data by prepending a short system-prompt instruction that deliberately elicits the undesirable trait. At test time, we evaluate without the instruction; inoculated models have much lower expression of the trait than models trained with unmodified training data. Inoculation is selective: in a toy setting where assistant responses are always in Spanish and ALL-CAPS, an appropriate inoculation (e.g., "You always speak in Spanish.") teaches the model to capitalize responses while still responding in English. We find that inoculation is effective across several additional settings: reducing emergent misalignment (EM) from narrow finetuning, defending against backdoor attacks, and mitigating the transmission of traits via subliminal learning. Follow-up analysis suggests a mechanism: making a trait less surprising in-context reduces optimization pressure to globally update the model, thereby reducing the degree of generalization. In the EM setting, we also show that inoculation explains prior results with educational insecure code. Beyond demonstrating a simple and effective technique for selective learning, our results contribute to a better conceptual understanding of how and why language models generalize.
3D Scene Prompting for Scene-Consistent Camera-Controllable Video Generation
Authors: JoungBin Lee; Jaewoo Jung; Jisang Han; Takuya Narihira; Kazumi Fukuda; Junyoung Seo; Sunghwan Hong; Yuki Mitsufuji; Seungryong Kim
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present 3DScenePrompt, a framework for camera-controllable video generation that maintains scene consistency when extending arbitrary-length input videos along user-specified trajectories. Unlike existing video generative methods limited to conditioning on a single image or just a few frames, we introduce a dual spatio-temporal conditioning strategy that fundamentally rethinks how video models should reference prior content. Our approach conditions on both temporally adjacent frames for motion continuity and spatially adjacent content for scene consistency. However, when generating beyond temporal boundaries, directly using spatially adjacent frames would incorrectly preserve dynamic elements from the past. We address this through introducing a 3D scene memory that represents exclusively the static geometry extracted from the entire input video. To construct this memory, we leverage dynamic SLAM with our newly introduced dynamic masking strategy that explicitly separates static scene geometry from moving elements. The static scene representation can then be projected to any target viewpoint, providing geometrically-consistent warped views that serve as strong spatial prompts while allowing dynamic regions to evolve naturally from temporal context. This enables our model to maintain long-range spatial coherence and precise camera control without sacrificing computational efficiency or motion realism. Extensive experiments demonstrate that our framework significantly outperforms existing methods in scene consistency, camera controllability, and generation quality.
UME-R1: Exploring Reasoning-Driven Generative Multimodal Embeddings
Authors: Zhibin Lan; Liqiang Niu; Fandong Meng; Jie Zhou; Jinsong Su
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The remarkable success of multimodal large language models (MLLMs) has driven advances in multimodal embeddings, yet existing models remain inherently discriminative, limiting their ability to benefit from reasoning-driven generation paradigm. In this work, we pioneer the exploration of reasoning-driven generative embeddings, unifying embedding tasks within a generative paradigm. We propose UME-R1, a universal multimodal embedding framework consisting of a two-stage training strategy: a cold-start supervised fine-tuning equips the model with reasoning capabilities and enables it to generate both discriminative and reasoning-driven generative embeddings; a subsequent reinforcement learning enhances reasoning and further optimizes generative embedding quality. This pioneering work reveals four key insights: 1) reasoning-driven generative embeddings unlock substantial performance gains over conventional discriminative embeddings by leveraging the powerful generative reasoning capabilities of MLLMs; 2) discriminative and reasoning-driven generative embeddings are complementary, whose combined oracle performance far exceeding that of either alone; 3) RL can effectively enhance reasoning-driven generative embeddings, establishing a scalable optimization paradigm; 4) repeated sampling at inference boosts downstream task coverage (pass@k), highlighting the inference-time scalability potential of reasoning-driven generative embeddings. Evaluated on the MMEB-V2 benchmark across 78 tasks spanning video, image, and visual documents, UME-R1 significantly outperforms conventional discriminative embedding models and offers a foundation for more interpretable, reasoning-driven generative multimodal embeddings. Our datasets, models, and code are available at https://github.com/XMUDeepLIT/UME-R1.