Data and Environment Matter More Than the Algorithm

The most counterintuitive takeaway from the industry: off-the-shelf RL algorithms are good enough to apply; success is determined by the fidelity of the simulation environment, the quality of the data, and the capabilities of the base model. If the environment is flawed – the policy will learn a “strategy for the exam”. Data quality matters more than the algorithm: noisy SFT data will bake in the noise; biased reward will steer RL astray. In many scenarios, good SFT data is sufficient – RL isn’t needed at all. Order of effort: strong base model → environment and data → then marginal algorithm optimization.

Related: [Simulation Environment], [Three Stages of Post-training], [Reward for Process vs. Reward for Outcome]