AlphaGeometry
DeepMind's AlphaGeometry is a theorem prover that solves Olympiad-level geometry problems at near gold-medallist performance, and crucially, without needing any human demonstrations.
AlphaCodium
AlphaCodium is a test-based, iterative "flow" that turns off-the-shelf LLMs into strong competitive-programming solvers without model training.
RAG vs. Finetuning
Microsoft researchers systematically compare RAG and fine-tuning (and their combination) on LLMs like Llama 2 and GPT-4 using an agricultural domain dataset.
Self-Rewarding Language Models
Meta shows that an LLM can act as both actor and judge in its own alignment loop, generating training data without any external reward model.
Tuning Language Models by Proxy
Proxy-tuning steers a large frozen LLM by *decoding-time* logit arithmetic using a much smaller fine-tuned model as a "proxy".
ReFT (Reinforced Fine-Tuning)
ByteDance's ReFT enhances LLM reasoning by combining supervised fine-tuning with online RL that samples alternative reasoning paths, without a learned reward model.
Overview of LLMs for Evaluation
A thorough survey of LLM-as-a-Judge and LLM-based evaluation methodologies, mapping strengths, limitations, and open problems.
Patchscopes
Patchscopes is a general framework for inspecting and intervening on LLM internals by "patching" hidden representations into a second inference pass.
Easy-to-Hard Generalization
UNC researchers show that LLMs often generalize well from easy training data to hard evaluation data, with implications for scalable oversight.
MoE-Mamba
MoE-Mamba combines state-space models (Mamba) with Mixture-of-Experts to scale LLMs more efficiently than either Mamba or Transformer-MoE alone.
Get next week’s papers.
The same picks and the same summaries, in your inbox. Free, and 176 issues deep.
Subscribe on Substack