While language models (LMs) excel in natural language tasks, their ability to learn impossible languages--those that violate the structural, cognitive, or logical constraints that make human languages learnable and functional--remains debatable. This controversy is further fueled by a divide between linguistic theorists and empirical researchers working with small-scale LMs. To address this controversy, we introduce semantic coherence. This concept measures how effectively semantic information is expressed in language structures and examines the learning capabilities of LMs across multiple scales. By fine-tuning billion-parameter LMs on impossible language samples, we demonstrate that larger LMs achieve similar performance on impossible and possible languages, whereas million-parameter LMs exhibit significant degradation when trained on impossible languages. This performance gap widens as semantic complexity increases. We further show that scaling up the LM size fundamentally shifts the learning focus from grammatical rules to semantic understanding, with billion-scale LMs exhibiting a threefold reduction in sensitivity to low-level linguistic perturbations while maintaining stable performance under high-level semantic perturbations.
Xiangchi Yuan, Xingjian Diao, Zheyuan Liu, Chunhui Zhang, Dachuan Shi, Kejing Xia, Zhenbang Du, Shenglong Yao 等 10 位作者
LLM post-training often optimizes multiple objectives with unequal priorities, such as correctness and reasoning efficiency. However, the widely used additive reward aggregation in RL can violate these priorities: when primary and auxiliary advantages conflict, a sufficiently large auxiliary signal can overturn the preference induced by the primary objective. We term this problem \emph{advantage priority inversion}. Although reward-level gating can protect priority more directly, it still removes potentially useful auxiliary distinctions among rollout samples that fail the primary condition. To address this trade-off, we propose Primary Advantage Policy Optimization (\textbf{PAPO}), a priority-aware advantage aggregation method that asymmetrically combines a primary and an auxiliary advantage. PAPO retains auxiliary supervision when the objectives agree and smoothly attenuates conflicting auxiliary signals, preserving the primary sign during fusion for \emph{primary-consistent ordering}. This rule applies to sequence-level advantages in GRPO and token-level advantages in actor--critic PPO. Experiments on multiple domains and models show that PAPO consistently achieves the best primary objective while maintaining best or competitive auxiliary-objective performance across both GRPO- and PPO-based optimization. \href{https://xiangchi-yuan.github.io/papo-project/}{Code is available}.
Xiangchi Yuan ByteDance Inc., Dachuan Shi Meta Superintelligence Labs and Georgia Institute of Technology, Chunhui Zhang Dartmouth College, Zheyuan Liu University of Notre Dame, Shenglong Yao, Soroush Vosoughi Dartmouth College, Wenke Lee Georgia Institute of Technology
Reinforcement learning (RL) is central to post-training, particularly for agentic models that require specialized reasoning behaviors. In this setting, model merging offers a practical mechanism for integrating multiple RL-trained agents from different tasks into a single generalist model. However, existing merging methods are designed for supervised fine-tuning (SFT), and they are suboptimal to preserve task-specific capabilities on RL-trained agentic models. The root is a task-vector mismatch between RL and SFT: on-policy RL induces task vectors that are highly sparse and heterogeneous, whereas SFT-style merging implicitly assumes dense and globally comparable task vectors. When standard global averaging is applied under this mismatch, RL’s non-overlapping task vectors that encode critical task-specific behaviors are reduced and parameter updates are diluted. To address this issue, we propose Reinforced Agent Merging (RAM), a distribution-aware merging framework explicitly designed for RL-trained agentic models. RAM disentangles shared and task-specific unique parameter updates, averaging shared components while selectively preserving and rescaling unique ones to counteract parameter update dilution. Experiments across multiple agent domains and model architectures demonstrate that RAM not only surpasses merging baselines, but also unlocks synergistic potential among agents to achieve performance superior to that of specialized agents in their domains.
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cs.CL · cs.AI
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Manzi Fabrice Niyigaba, Ivory Yang Dartmouth College, Soroush Vosoughi Dartmouth College
Propaganda is a widely used approach for shaping public opinion and disseminating misinformation in news media. While it has recently gained significant attention within the NLP community, research on fine grained propaganda detection remains heavily concentrated in high resource languages. To bridge this gap, we introduce KinyaProp, the first fine-grained propaganda dataset of its kind for Kinyarwanda and, to our knowledge, the first such resource created for a Bantu language. Using this dataset, we evaluate whether state-of-the-art LLMs can function as reliable annotators in a genuinely low resource and culturally grounded setting. Our results show that current multilingual LLMs do not reliably approximate human annotation behavior. Instead, they behave as conservative annotators whose performance is largely limited to lexically explicit cues, substantially under-identifying propaganda and exhibiting extremely low and unstable performance on discourse-level techniques. Our findings highlight an important limitation of recent successes in LLM based annotation reported for high resource languages, demonstrating that such results do not readily transfer to low resource settings, where scalable annotation would be most valuable. We release KinyaProp to support future research on fine grained propaganda detection and to enable more robust evaluation of multilingual models in underrepresented languages.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Curriculum learning (CL), which orders training data from easy to hard, has become a popular strategy for improving reasoning in large language models (LLMs). Yet prior work employs disparate difficulty metrics and training setups, leaving open fundamental questions: When does curriculum help? Which direction—forward or reverse—is better? And does the answer depend on what we measure? We address these questions through a unified offline evaluation framework that decomposes curriculum difficulty into five complementary dimensions: Problem Difficulty, Model Surprisal, Confidence Margin, Predictive Uncertainty, and Decision Variability. Through controlled post-training experiments on mathematical reasoning benchmarks with Llama3.1-8B, Mistral-7B, and Gemma3-4B, we find that: (i) no curriculum strategy dominates universally—the relative effectiveness of forward versus reverse CL depends jointly on model capability and task complexity; (ii) even within a single metric, samples at different difficulty levels produce distinct gains depending on task demands; and (iii) Task-aligned curricula focus on shaping the model’s final representations and generalization, whereas inner-state curricula modulate internal states such as confidence and uncertainty. Our findings challenge the notion of a universal curriculum strategy and offer actionable guidance across model and task regimes, with some metrics indicating that prioritizing decision-uncertain samples can further enhance learning outcomes.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Zhongyu Ouyang, Qianlong Wen ByteDance Inc., Chunhui Zhang Dartmouth College, Yanfang Ye University of Notre Dame, Soroush Vosoughi Dartmouth College
What enables large language models (LLMs) to effectively model user preferences in sequential recommendation? Our investigation reveals that existing preference-alignment approaches largely rely on binary pairwise comparisons, overlooking two critical factors: preference intensity (the structured strength of affinity or aversion) and temporal context (the extent to which recent interactions better reflect a user’s current intent). Through controlled experiments, we show that leveraging comprehensive feedback with structured preference signals substantially improves recommendation performance, indicating that binary modeling discards essential information. Motivated by these findings, we propose RecPO, a unified preference optimization framework that maps both explicit and implicit feedback into a common preference signal and constructs adaptive reward margins that jointly account for preference intensity and interaction recency. Experiments across five datasets show that RecPO consistently outperforms state-of-the-art baselines while exhibiting behavioral patterns aligned with human decision-making, including favoring immediate satisfaction, maintaining preference coherence, and avoiding dispreferred items. Our results highlight that preference intensity and temporal context are fundamental ingredients for effective LLM-based recommendation. Code: https://github.com/zyouyang/RecPO
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Elisaveta Samoylov, Soroush Vosoughi Dartmouth College
Lean proofs are built as sequences of tactic-induced state transitions, yet learned models often represent proof steps primarily through tactic strings or raw proof-state text. Building on Delta Tokens, which encode a proof step by the local edit it induces between successive proof states, we train an encoder-only Transformer to learn contextualized representations of Lean proof steps from state changes. We then use these step representations to study complete proofs as trajectories in a learned latent space.We first show that the Delta-based Transformer yields better held-out next-tactic retrieval than a matched surface-syntax control, supporting the representational choice used in the trajectory analysis. We then analyze proof trajectories using path length, endpoint span, directness, curvature, and torsion. Across the LeanWorkbook slice used here, longer proofs become increasingly indirect within a relatively bounded latent span: path length grows sharply with proof length while endpoint span changes little, mean step size decreases, curvature rises modestly, and torsion falls. Qualitative case studies show that these geometric patterns align with recognizable proof organizations, including immediate closure, aligned accumulation, scaffolded enrichment, bookkeeping-heavy restructuring, and repeated local contradiction work.The dataset is small and heavily skewed toward short proofs, so the claims are necessarily limited. Within those limits, the results suggest that learned state-change representations recover nontrivial structure in how proofs unfold and provide a promising basis for future trajectory-aware theorem proving.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表