arXiv AI
Jul 28
LEDOM: Reverse Language Model
arXiv:2507. 01335v4 Announce Type: replace-cross Abstract: Autoregressive language models are trained exclusively left-to-right.
Breaking the Reversal Curse in Autoregressive Language Models via Identity Bridge
arXiv:2602. 02470v2 Announce Type: replace Abstract: Autoregressive large language models (LLMs) have achieved remarkable success in many complex tasks, yet they can still fail in very simple logical reasoning such as the "reversal curse" -- when trained on forward knowledge data of the form "$A \rightarrow B$" (e.
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arXiv AI
Jun 8
Data-Efficient Autoregressive-to-Diffusion Language Models via On-Policy Distillation
arXiv:2606. 06712v1 Announce Type: cross Abstract: We study the transformation of autoregressive models (ARLMs) into diffusion language models (DLMs).
arXiv AI
Aug 7
DASH: Divergence-Adaptive Supervision Horizons for On-Policy Self-Distillation of Reasoning Models
arXiv:2608. 06243v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models using automatically verifiable outcome signals, but these signals are typically sparse and at the sequence-level.
arXiv AI
Jul 10
ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation
arXiv:2502. 15543v4 Announce Type: replace-cross Abstract: Large language models (LLMs) integrated with retrieval-augmented generation (RAG) have improved factuality by grounding outputs in external evidence.
arXiv Machine Learning
1d ago
MoRFI: Monotonic Sparse Autoencoder Feature Identification
arXiv:2604. 26866v2 Announce Type: replace-cross Abstract: Large language models (LLMs) acquire most of their factual knowledge during the pre-training stage, through next token prediction.
arXiv Machine Learning
Jun 11
Counterexample Guided Learning in the Large using Reasoning Agents
arXiv:2606. 11521v1 Announce Type: new Abstract: LLMs and LLM agents should improve when given feedback, but identifying when they are able to do so is difficult: feedback is heterogeneous, domain-specific, and difficult to control.
arXiv Machine Learning
Aug 4
Self-Correction Bench: Uncovering and Addressing the Self-Correction Blind Spot in Large Language Models
arXiv:2507. 02778v3 Announce Type: replace-cross Abstract: Although large language models (LLMs) have transformed AI, they still make errors and follow unproductive reasoning paths.
Hugging Face Trending Papers
Jun 8
Multi-Hop Knowledge Composition is Bound by Pretraining Exposure
Large Language Models fail at implicit multi-hop reasoning: a model answers "When was $X$ born? " and "Who is $Y$'s closest friend?
arXiv Machine Learning
Aug 3
Reflection or Re-Generation? Why LLM Revision Fails Where Human Revision Succeeds
arXiv:2607. 28908v1 Announce Type: new Abstract: Reflection, the ability to revisit and revise prior reasoning, is central to how humans improve their answers.
arXiv AI
Aug 5
ReflectRL: Learning from Golden Negative Trajectories via Reflective-to-Direct Reasoning
arXiv:2608. 03972v1 Announce Type: new Abstract: On-policy training has emerged as a powerful post-training paradigm for improving the reasoning capabilities of large language models, and is often enhanced by golden trajectories from stronger expert models.
arXiv AI
Jul 3
Purified OPSD: On-Policy Self-Distillation Without Losing How to Think
arXiv:2607. 02234v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) has emerged as a promising paradigm for improving LLM reasoning, where a privileged teacher with access to reference solutions provides token-level supervision on the student's own generated trajectories.
arXiv AI
Jul 20
Understanding Reasoning from Pretraining to Post-Training
arXiv:2607. 16097v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become central to improving large language models (LLMs) on complex reasoning tasks, yet RL post-training is largely studied in isolation from the pretraining that precedes it.