Optimising Computation At The Token-Level
Optimising computation at the token-level
reference
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Learning to Reason without External Rewards
aka Self-Confidence is All You Need
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John Carmack is working on game-playing robots
on John Carmack's Upperbound 25 Talk Notes
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Vibe-Coding Mathematical Discoveries
Using evolutionary algorithms with LLM-coding agents
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NoProp: Training Neural Networks Without Back-Propagation or Forward-Propagation
an alternative training method to backprop that does local layer learning
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Absolute Zero: Reinforced Self-play Reasoning with Zero Data
learn to reason without any human-annotated data.
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Playing Atari with Deep Reinforcement Learning
a classic paper applying neural networks to RL for game playing
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Large Language Models are Zero-Shot Reasoners (May 2022)
improve zero-shot prompt performance of LLMs by adding “Let’s think step by step” before each answer
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Neural Machine Translation by Jointly Learning to Align and Translate (Sep 2014)
improve the Encoder/Decoder alignment with an Attention Mechanism
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Thinking LLMs: General Instruction Following with Thought Generation (Oct 2024)
a prompting and fine-tuning method that enables LLMs to engage in a "thinking" process before generating responses