prime-rl gets an Algorithms layer

Prime-rl has introduced a first-class algorithms layer designed to centralize algorithm-specific elements within its reinforcement learning framework, which now supports six built-in algorithms—GRPO, MaxRL, On-Policy Distillation, Self-Distillation, SFT distillation, and ECHO. This new layer allows for algorithm selection on a per-environment basis, enabling a single run to train different algorithms on different environments without altering the core trainer, thus enhancing flexibility and performance. The centralization is achieved by organizing each algorithm as a module under a common orchestrator directory, simplifying the understanding and modification of algorithms without compromising system performance. This setup allows researchers to innovate by subclassing a common abstraction without delving into trainer internals and supports multi-teacher distillation, enabling environments to utilize specialized teacher models for more domain-specific training. By resolving algorithms per environment rather than per run, prime-rl offers a unique approach not commonly found in other open-source RL frameworks, facilitating more targeted and effective training signals based on the specific properties of each environment.

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