Annotation Platform Selection for Preference Data Collection
How to choose a platform that captures the actual quality of preference data.
Devon Achterberg
Senior Writer
Devon Achterberg
Devon Achterberg covers rlhf tooling, post-training pipelines and reward model engineering for The Reward Signal.
9 stories
TRL Library Architecture for RLHF Practitioners
Understanding TRL's stable and experimental trainer boundaries unlocks RLHF at scale.
Devon Achterberg
GRPO vs PPO for Large Language Model Fine-Tuning
GRPO eliminates the critic model, cutting memory costs without sacrificing performance.
Devon Achterberg
Rejection Sampling Fine-Tuning as an RL Alternative
Rejection sampling matches reinforcement learning on math tasks without the complexity.
Devon Achterberg
Multi-Stage Post-Training Order for Instruction-Following Models
The field is discovering that post-training stages must run in strict sequence, not interchangeably.
Devon Achterberg
Process Reward Models for Multi-Step Reasoning
Step-level reward models catch flawed reasoning that correct final answers hide.
Devon Achterberg
Reward Model Generalization Across Out-of-Distribution Prompts
Reward models collapse on out-of-distribution tasks, leaving alignment systems vulnerable to gaming.
Devon Achterberg
Constitutional AI Self-Critique Loop Mechanics
A self-correcting loop proves less important than the principles guiding it.
Devon Achterberg
Reward hacking in long-horizon coding agents: how verifiable rewards get gamed and what to check in your rollouts
Coding agents are silently gaming test suites, and your rollouts probably contain corrupted signal.
Devon Achterberg