GenAI4ELab/papercli-papers huggingface dataset:
Towards Greater Leverage: Scaling Laws for Efficient Mixture-of-Experts Language Models
Authors: Changxin Tian; Kunlong Chen; Jia Liu; Ziqi Liu; Zhiqiang Zhang; JUN ZHOU
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Mixture-of-Experts (MoE) has become a dominant architecture for scaling Large Language Models (LLMs) efficiently by decoupling total parameters from computational cost. However, this decoupling creates a critical challenge: predicting the model capacity of a given MoE configurations (e.g., expert activation ratio and granularity) remains an unresolved problem. To address this gap, we introduce Efficiency Leverage (EL), a metric quantifying the computational advantage of an MoE model over a dense equivalent. We conduct a large-scale empirical study, training over 300 models up to 28B parameters, to systematically investigate the relationship between MoE architectural configurations and EL. Our findings reveal that EL is primarily driven by the expert activation ratio and the total compute budget, both following predictable power laws, while expert granularity acts as a non-linear modulator with a clear optimal range. We integrate these discoveries into a unified scaling law that accurately predicts the EL of an MoE architecture based on its configuration. 
To validate our derived scaling laws, we designed and trained MoE-mini, a model with only 0.85B active parameters, alongside a 6.1B dense model for comparison. When trained on an identical 1T high-quality token dataset, MoE-mini matched the performance of the 6.1B dense model while consuming over 7x fewer computational resources, thereby confirming the accuracy of our scaling laws.
This work provides a principled and empirically-grounded foundation for the scaling of efficient MoE models.
CyclicReflex: Improving Reasoning Models via Cyclical Reflection Token Scheduling
Authors: Chongyu Fan; Yihua Zhang; Jinghan Jia; Alfred O. Hero; Sijia Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large reasoning models (LRMs), such as OpenAI’s o1 and DeepSeek-R1, harness test-time scaling to perform multi-step reasoning for complex problem-solving. This reasoning process, executed before producing final answers, is often guided by special juncture tokens that prompt self-evaluative reflection. These transition markers and reflective cues are referred to as “reflection tokens” (e.g., “wait”, “but”, “alternatively”). In this work, we treat reflection tokens as a “resource” and introduce the problem of resource allocation, aimed at improving the test-time compute performance of LRMs by adaptively regulating the frequency and placement of reflection tokens. Through empirical analysis, we show that both excessive and insufficient use of reflection tokens, referred to as over-reflection and under-reflection, can degrade model performance. To better understand this trade-off, we draw an analogy between reflection token usage and learning rate scheduling in optimization. Building on this insight, We propose cyclical reflection token scheduling (termed CyclicReflex), a training-free decoding strategy that dynamically modulates reflection token logits with a bidirectional, position-dependent triangular waveform, incurring no additional computation cost. Experiments on MATH500, AIME2024/2025, AMC2023, GPQA Diamond and LiveCodeBench demonstrate that CyclicReflex consistently improves performance across model sizes (1.5B–14B), outperforming standard decoding and recent approaches such as TIP (thought switching penalty) and S1.
CodeSense: a Real-World Benchmark and Dataset for Code Semantic Reasoning
Authors: Monoshi Kumar Roy; Simin Chen; Benjamin Steenhoek; Jinjun Peng; Gail Kaiser; Baishakhi Ray; Wei Le
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Understanding and reasoning about code semantics is essential for enhancing code LLMs' abilities to solve real-world software engineering (SE) tasks. Although several code reasoning benchmarks exist, most rely on synthetic datasets or educational coding problems and focus on coarse-grained reasoning tasks such as input/output prediction, limiting their effectiveness in evaluating LLMs in practical SE contexts. To bridge this gap, we propose CodeSense, the first benchmark that makes available a spectrum of fine-grained code reasoning tasks concerned with the software engineering of real-world code. We collected Python, C and Java software projects from real-world repositories. We executed tests from these repositories, collected their execution traces, and constructed a ground truth dataset for fine-grained semantic reasoning tasks.  We then performed comprehensive evaluations on state-of-the-art LLMs. Our results show a clear performance gap for the models to handle fine-grained reasoning tasks. Although prompting techniques such as chain-of-thought and in-context learning helped, the lack of code semantics in LLMs fundamentally limit models' capabilities of code reasoning. Besides dataset, benchmark and evaluation, our work produced an execution tracing framework and tool set that make it easy to collect ground truth for fine-grained SE reasoning tasks, offering a strong basis for future benchmark construction and model post training. Our code and data are located at \url{https://codesense-bench.github.io/}.
Supervised Reinforcement Learning: From Expert Trajectories to Step-wise Reasoning
Authors: Yihe Deng; I-Hung Hsu; Jun Yan; Zifeng Wang; Rujun Han; Gufeng Zhang; Yanfei Chen; Wei Wang; Tomas Pfister; Chen-Yu Lee
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) often struggle with problems that require multi-step reasoning. For small-scale open-source models, Reinforcement Learning with Verifiable Rewards (RLVR) fails when correct solutions are rarely sampled even after many attempts, while Supervised Fine-Tuning (SFT) tends to overfit long demonstrations through rigid token-by-token imitation. To address this gap, we propose Supervised Reinforcement Learning (SRL), a framework that reformulates problem solving as generating a sequence of logical ``actions''. SRL trains the model to generate an internal reasoning monologue before committing to each action. It provides smoother rewards based on the similarity between the model's actions and expert actions extracted from the SFT dataset in a step-wise manner. This supervision offers richer learning signals even when all rollouts are incorrect, while encouraging flexible reasoning guided by expert demonstrations. As a result, SRL enables small models to learn challenging problems previously unlearnable by SFT or RLVR. Moreover, initializing training with SRL before refining with RLVR yields the strongest overall performance. Beyond reasoning benchmarks, SRL generalizes effectively to agentic software engineering tasks, establishing it as a robust and versatile training framework for reasoning-oriented LLMs.
Why Less is More (Sometimes): A Theory of Data Curation
Authors: Elvis Dohmatob; Mohammad Pezeshki; Reyhane Askari-Hemmat
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This paper introduces a theoretical framework to resolve a central paradox in modern machine learning: When is it better to use less data? This question has become critical as classical scaling laws suggesting ``more is more'' (Sun et al., 2025) are challenged by methods like LIMO (``less is more'') and s1 (Ye et al., 2025; Muenighoff et al., 2025), which achieve superior performance with small, aggressively curated datasets. Here, we study data curation strategies where an imperfect oracle selects the training examples according to their difficulty and correctness. Our results provide exact scaling law curves for test error under both label-agnostic and label-aware curation rules, revealing when and why keeping only a subset of data can improve generalization. In contrast to classical scaling laws, we show that under certain conditions, small curated datasets can outperform full datasets, and we provide analytical conditions for this by deriving precise phase transition curves tied to data size and quality. We validate these theoretical claims with empirical results on ImageNet, confirming our predictions about when curation improves accuracy and can even mitigate model collapse. Furthermore, our framework provides a principled explanation for the contradictory curation strategies recently observed in LLM mathematical reasoning.
AutoSP: Unlocking Long-Context LLM Training Via Compiler-Based Sequence Parallelism
Authors: Ahan Gupta; Zhihao Wang; Neel Dani; Masahiro Tanaka; Olatunji Ruwase; Minjia Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large-language-models (LLMs) demonstrate enormous utility in long-context tasks which require processing prompts that consist of tens to hundreds of thousands of tokens. However, existing LLM training libraries do not provide easy to use abstractions to optimize for long-context training, instead focusing on optimizations for models with large parameter counts through ZeRO-3/FSDP, Tensor and Pipeline parallelism. This forces users to rewrite LLM training libraries to incorporate compositions of various complex long-context optimizations, such as sequence-parallelism, to training pipelines; a process that requires in-depth expertise, reducing developer productivity. To tackle these challenges, we introduce AutoSP: the first automated solution to automatically optimize LLM training for longer-contexts. AutoSP compiles models and applies a targeted set of optimizations: automated sequence parallelism, and long-context aware activation-checkpointing, to drastically enhance LLM trainability at negligible cost to throughput. Our evaluation demonstrates AutoSP's capability on both NVIDIA and AMD hardware, increasing training contexts by upto 2.7$\times$ and 2.5$\times$ respectively over competitive hand-written baseline at negligible cost to runtime performance.
PrefDisco: Benchmarking Proactive Personalized Reasoning
Authors: Shuyue Stella Li; Avinandan Bose; Faeze Brahman; Simon Shaolei Du; Pang Wei Koh; Maryam Fazel; Yulia Tsvetkov
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Current large language model (LLM) development treats task-solving and preference-alignment as separate challenges, optimizing first for objective correctness, then for alignment to aggregated human preferences. This paradigm fails in human-facing applications where solving a problem correctly is insufficient if the response mismatches the user’s needs. This challenge intensifies in just-in-time scenarios where no prior user interaction history exists due to cold-start conditions or privacy constraints. LLMs need to proactively identify what they don’t know about the user, strategically elicit preference values through questioning, then adapt their reasoning processes and responses accordingly—a complicated chain of cognitive processes which we term personalized reasoning. We introduce PREFDISCO, an evaluation methodology that transforms static benchmarks into interactive personalization tasks using psychologically-grounded personas with sparse, context-dependent preferences, and define PREFALIGN as a fine-grained rubric-based metric for measuring preference alignment. PREFDISCO builds scenarios where identical questions require different reasoning chains depending on user context, as optimal explanation approaches vary by individual expertise and preferences while maintaining factual accuracy. Evaluation of 21 frontier models across 10 tasks reveals 29.0% of naive personalization attempts produce worse preference alignment than generic responses, yet generic responses also fail to serve individual user needs. These findings suggest personalized reasoning requires dedicated development rather than emerging naturally. PREFDISCO provides a foundation for developing systems that can adapt to individual users in education, healthcare, and technical domains where personalization is critical.
Robust Decision-Making with Partially Calibrated Forecasters
Authors: Shayan Kiyani; Hamed Hassani; George J. Pappas; Aaron Roth
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Calibration has emerged as a foundational goal in trustworthy machine learning, in part because of its strong decision theoretic semantics. Independent of the underlying distribution, and independent of the decision maker's utility function, calibration promises that amongst all policies mapping predictions to actions, the uniformly best policy is the one that trusts the predictions and acts as if they were correct. But this is true only of fully calibrated forecasts, which are tractable to guarantee only for very low dimensional prediction problems. For higher dimensional prediction problems (e.g. when outcomes are multiclass), weaker forms of calibration have been studied that lack these decision theoretic properties. In this paper we study how a conservative decision maker should map predictions endowed with these weaker (partial) calibration guarantees to actions, in a way that is robust in a minimax sense: i.e. to maximize their expected utility in the worst case over distributions consistent with the calibration guarantees. We characterize their minimax optimal decision rule via a duality argument, and show that surprisingly, trusting the predictions and acting accordingly is recovered in this minimax sense by decision calibration (and any strictly stronger notion of calibration), a substantially weaker and more tractable condition than full calibration. For calibration guarantees that fall short of decision calibration, the minimax optimal decision rule is still efficiently computable, and we provide an empirical evaluation of a natural one that applies to any regression model solved to optimize squared error.
Exploratory Causal Inference in SAEnce
Authors: Tommaso Mencattini; Riccardo Cadei; Francesco Locatello
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Randomized Controlled Trials are one of the pillars of science; nevertheless, they rely on hand-crafted hypotheses and expensive analysis. Such constraints prevent causal effect estimation at scale, potentially anchoring on popular yet incomplete hypotheses. We propose to discover the unknown effects of a treatment directly from data. For this, we turn unstructured data from a trial into meaningful representations via pretrained foundation models and interpret them via a Sparse Auto Encoder. However, discovering significant causal effects at the neural level is not trivial due to multiple-testing issues and effects entanglement. To address these challenges, we introduce _Neural Effect Search_, a novel recursive procedure solving both issues by progressive stratification. After assessing the robustness of our algorithm on semi-synthetic experiments, we showcase, in the context of experimental ecology, the first successful unsupervised causal effect identification on a real-world scientific trial.
Unified 3D Scene Understanding Through Physical World Modeling
Authors: Wanhee Lee; Klemen Kotar; Rahul Mysore Venkatesh; Jared Watrous; Honglin Chen; Khai Loong Aw; Daniel LK Yamins
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Understanding 3D scenes requires flexible combinations of visual reasoning tasks, including depth estimation, novel view synthesis, and object manipulation, all of which are essential for perception and interaction. Existing approaches have typically addressed these tasks in isolation, preventing them from sharing a common representation or transferring knowledge across tasks. A conceptually simpler but practically non-trivial alternative is to unify these diverse tasks into a single model, reducing different tasks from separate training objectives to merely different prompts and allowing for joint training across all datasets. In this work, we present a physical world model for unified 3D understanding and interaction 3WM, formulated as a probabilistic graphical model in which nodes represent multimodal scene elements such as RGB, optical flow, and camera pose. Diverse tasks emerge from different inference pathways through the graph: novel view synthesis from RGB and dense flow prompts, object manipulation from RGB and sparse flow prompts, and depth estimation from RGB and camera conditioning, all zero-shot without task-specific training. 3WM outperforms specialized baselines without the need for finetuning by offering precise controllability, strong geometric consistency, and robustness in real-world scenarios, achieving state-of-the-art performance on NVS and 3D object manipulation. Beyond predefined tasks, the model supports composable inference pathways, such as moving objects aside while navigating a 3D environment, enabling complex geometric reasoning. This demonstrates that a unified model can serve as a practical alternative to fragmented task-specific systems, taking a step towards a general-purpose visual world model.
Infinite Horizon Markov Economies
Authors: Denizalp Goktas; Sadie Zhao; Yiling Chen; Amy Greenwald
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In this paper, we study a generalization of Markov games and pseudo-games that we call Markov pseudo-games, which like the former, captures time and uncertainty, and like the latter, allows for the players’ actions to determine the set of actions available to the other players. In the same vein as Arrow and Debreu, we intend for this model to be rich enough to encapsulate a broad mathematical framework for modeling economies. We then prove the existence of a game-theoretic equilibrium in our model, which in turn implies the existence of a general equilibrium in the corresponding economies. Finally, going beyond Arrow and Debreu, we introduce a solution method for Markov pseudo-games, and prove its polynomial-time convergence.

We then provide an application of Markov pseudo-games to infinite-horizon Markov exchange economies, a stochastic economic model that extends Radner’s stochastic exchange economy and Magill and Quinzii’s infinite horizon incomplete markets model. We show that under suitable assumptions, the solutions of any infinite horizon Markov exchange economy (i.e., recursive Radner equilibria—RRE) can be formulated as the solution to a concave Markov pseudo-game, thus establishing the existence of RRE, and providing first-order methods for approximating RRE. Finally, we demonstrate the effectiveness of our approach in practice by building the corresponding generative adversarial policy neural network, and using it to compute RRE in a variety of infinite-horizon Markov exchange economies.
Modal Aphasia: Can Unified Multimodal Models Describe Images From Memory?
Authors: Michael Aerni; Joshua Swanson; Kristina Nikolić; Florian Tramèr
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present *modal aphasia*, a systematic dissociation in which current unified multimodal models accurately memorize concepts visually but fail to articulate them in writing, despite being trained on images and text simultaneously. For one, we show that leading frontier models can generate near-perfect reproductions of iconic movie artwork, but confuse crucial details when asked for textual descriptions. We corroborate those findings through controlled experiments on synthetic datasets in multiple architectures. Our experiments confirm that modal aphasia reliably emerges as a fundamental property of current unified multimodal models, not just as a training artifact. In practice, modal aphasia can introduce vulnerabilities in AI safety frameworks, as safeguards applied to one modality may leave harmful concepts accessible in other modalities. We demonstrate this risk by showing how a model aligned solely on text remains capable of generating unsafe images.
AutoLibra: Agent Metric Induction from Open-Ended Human Feedback
Authors: Hao Zhu; Phil Cuvin; Xinkai Yu; Charlotte Ka Yee Yan; Jason Zhang; Diyi Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Agents are predominantly evaluated and optimized via task success metrics, which are coarse,
rely on manual design from experts, and fail to reward intermediate emergent behaviors.
We propose AutoLibra, a framework for agent evaluation, that transforms open-ended
human feedback e.g. “If you find that the button is disabled, don’t click it again”, or “This
agent has too much autonomy to decide what to do on its own” into metrics for evaluating
fine-grained behaviors in agent trajectories. AutoLibra accomplishes this by grounding
feedback to an agent’s behavior, clustering similar positive and negative behaviors, and
creating concrete metrics with clear definitions and concrete examples, which can be used for
prompting LLM-as-a-Judge as evaluators. We further propose two meta-metrics to evaluate
the alignment of a set of (induced) metrics with open feedback: “coverage” and “redundancy”.
Through optimizing these meta-metrics, we experimentally demonstrate AutoLibra’s ability
to induce more concrete agent evaluation metrics than the ones proposed in previous
agent evaluation benchmarks and discover new metrics to analyze agents. We also present
two applications of AutoLibra in agent improvement: First, we show that AutoLibra
serve human prompt engineers for diagonalize agent failures and improve prompts iterative.
Moreover, we find that AutoLibra can induce metrics for automatic optimization for agents,
which makes agents improve through self-regulation. Our results suggest that AutoLibra is a
powerful task-agnostic tool for evaluating and improving language agents.
LeRobot:  An Open-Source Library for End-to-End Robot Learning
Authors: Remi Cadene; Simon Alibert; Francesco Capuano; Michel Aractingi; Adil Zouitine; Pepijn Kooijmans; Jade Choghari; Martino Russi; Caroline Pascal; Steven Palma; Dana Aubakirova; Mustafa Shukor; Jess Moss; Alexander Soare; Quentin Lhoest; Quentin Gallouédec; Thomas Wolf
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Robotics is undergoing a significant transformation powered by advances in high-level control techniques based on machine learning, giving rise to the field of robot learning.
Recent progress in robot learning has been accelerated by the increasing availability of affordable teleoperation systems, large-scale openly available datasets, and scalable learning-based methods.
However, development in the field of robot learning is often slowed by fragmented, closed-source tools designed to only address specific sub-components within the robotics stack.
In this paper, we present lerobot, an open-source library that integrates across the entire robotics stack, from low-level middleware communication for motor controls to large-scale dataset collection, storage and streaming.
The library is designed with a strong focus on real-world robotics, supporting accessible hardware platforms while remaining extensible to new embodiments.
It also supports efficient implementations for various state-of-the-art robot learning algorithms from multiple prominent paradigms, as well as a generalized asynchronous inference stack.
Unlike traditional pipelines which heavily rely on hand-crafted techniques, lerobot emphasizes scalable learning approaches that improve directly with more data and compute.
Designed for accessibility, scalability, and openness, lerobot lowers the barrier to entry for researchers and practitioners to robotics while providing a platform for reproducible, state-of-the-art robot learning.