Reward Hacking in Autonomous Agent Training
How Goodhart's Law and Skalse's unhackability theorems explain reward hacking in RL and LLM agents, and how consequence-based selection changes the loop.
All
AI 16
Continuous Learning 15
LoRA 13
Fine-Tuning 11
Self-Training 8
SFT 7
Catastrophic Forgetting 6
Continual Learning 6
AI Agents 5
Data Selection 3
Continual Learning
Anchored SFT KL Divergence Fine Tuning: Unifying DFT and Trust Regions
Master anchored sft kl divergence fine tuning, combining probability-weighted loss with trust region sft lora and kl regularized supervised fine tuning.
Experience Replay to Stop Catastrophic Forgetting in LLMs
Master experience replay catastrophic forgetting llm solutions with complementary learning systems and rehearsal buffer LoRA fine-tuning strategies.
What Are Elastic Weights? EWC vs. LoRA in Continual Learning
Elastic Weight Consolidation (EWC) explained: how Fisher weight springs prevent catastrophic forgetting, why they struggle at LLM scale, and tools to try.
Continual Learning Under Self-Induced Distribution Shift
When an agent's actions alter its environment for good, cumulative replay causes catastrophic remembering; a sliding window sustains continual learning.
Afterlife: Weight-Level Medium-Term Memory for AIs
Afterlife is an open-source Agent Skill and CLI tool that lets local LLMs nominate memories, train and test LoRA adapters, and activate or roll back updates.