7 papers · 1 filter
Quantifying Subliminal Behavioral Transfer Ratios in Language Model Distillation
Uwe König, Hamza Kazmi, Ruizhe Li +1
Distillation of a language model intended to transfer benign behavior to a student model may also transfer undesirable characteristics, if they are present in the teacher model, a…
Mechanistic origins of catastrophic forgetting: why RL preserves circuits better than SFT?
Jeanmely Rojas Nunez, Viraj Sawant, Nathan Allen +4
Fine-tuning large language models (LLMs) frequently induces catastrophic forgetting of prior capabilities. Recent work has shown that reinforcement learning (RL) retains prior capa…
Broken Chains: The Cost of Incomplete Reasoning in LLMs
Ian Su, Gaurav Purushothaman, Jey Narayan +5
Reasoning-specialized models like OpenAI's 5.1 and DeepSeek-V3.2 allocate substantial inference compute to extended chain-of-thought (CoT) traces, yet reasoning tokens incur signif…
Alignment-Constrained Dynamic Pruning for LLMs: Identifying and Preserving Alignment-Critical Circuits
Dev Patel, Gabrielle Gervacio, Diekola Raimi +5
Large Language Models require substantial computational resources for inference, posing deployment challenges. While dynamic pruning offers superior efficiency over static methods…
PALADIN: Self-Correcting Language Model Agents to Cure Tool-Failure Cases
Sri Vatsa Vuddanti, Aarav Shah, Satwik Kumar Chittiprolu +4
Tool-augmented language agents frequently fail in real-world deployment due to tool malfunctions--timeouts, API exceptions, or inconsistent outputs--triggering cascading reasoning…
Amortized Latent Steering: Low-Cost Alternative to Test-Time Optimization
Nathan Egbuna, Saatvik Gaur, Sunishchal Dev +2
Test-time optimization remains impractical at scale due to prohibitive inference costs--techniques like iterative refinement and multi-step verification can require …