5 papers
Eliciting Weak-to-Strong Generalization with On-Policy Reverse Distillation
Youngrok Park, Sangmin Bae, Hojung Jung +6
Weak-to-strong generalization asks whether stronger models can learn from weaker supervisors and surpass them. This is particularly important for successive model generations and m…
Preserving Speech-to-Text LLM Capabilities in Speech-to-Speech Generation
Yuxuan Hu, Heng Lu, Ruchao Fan +8
Strong speech-to-text (S2T) LLMs already provide robust speech perception and text reasoning, but adding speech-to-speech (S2S) output is challenging: fine-tuning the backbone can…
Scaling Reasoning Efficiently via Relaxed On-Policy Distillation
Jongwoo Ko, Sara Abdali, Young Jin Kim +2
On-policy distillation (OPD) is a promising approach for transferring reasoning capabilities to capacity-constrained models, yet sampled-token OPD often suffers from entropy collap…
The Bias is in the Details: An Assessment of Cognitive Bias in LLMs
R. Alexander Knipper, Charles S. Knipper, Kaiqi Zhang +3
As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases.…
Decoder-Hybrid-Decoder Architecture for Efficient Reasoning with Long Generation
Liliang Ren, Congcong Chen, Haoran Xu +11
Recent advances in language modeling have demonstrated the effectiveness of State Space Models (SSMs) for efficient sequence modeling. While hybrid architectures such as Samba and…