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 quality SFT data is sufficient – RL is not needed at all.

Order of efforts: 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