Minimax enhances its reinforcement learning process by treating its own expert developers as scalable reward models. These developers participate directly in the training cycle, identifying desirable behaviors and providing precise feedback on complex coding tasks, which creates a model tailored to professional workflows.

Modern LLMs use a simple form of reinforcement learning that directly rewards successful outcomes. This contrasts with more sophisticated methods, like those in AlphaGo or the brain, which use "value functions" to estimate long-term consequences. It's a mystery why the simpler approach is so effective.

AI labs like Anthropic find that mid-tier models can be trained with reinforcement learning to outperform their largest, most expensive models in just a few months, accelerating the pace of capability improvements.

Model architecture decisions directly impact inference performance. AI company Zyphra pre-selects target hardware and then chooses model parameters—such as a hidden dimension with many powers of two—to align with how GPUs split up workloads, maximizing efficiency from day one.

Moonshot overcame the tendency of LLMs to default to sequential reasoning—a problem they call "serial collapse"—by using Parallel Agent Reinforcement Learning (PARL). They forced an orchestrator model to learn parallelization by giving it time and compute budgets that were impossible to meet sequentially, compelling it to delegate tasks.

Instead of a single "think then act" cycle, Minimax trains its M2 model to repeatedly pause and rethink after receiving feedback from the environment. This iterative "interleaved thinking" approach improves robustness and performance on long-horizon tasks where tool responses or conditions are unpredictable.

While prompt optimization is theoretically appealing, OpenPipe's team evaluated methods like JEPA and found they provided only minor boosts. Their RL fine-tuning methods delivered vastly superior results (96% vs 56% on a benchmark), suggesting weight updates still trump prompt engineering for complex tasks.

The distinction between imitation learning and reinforcement learning (RL) is not a rigid dichotomy. Next-token prediction in LLMs can be framed as a form of RL where the "episode" is just one token long and the reward is based on prediction accuracy. This conceptual model places both learning paradigms on a continuous spectrum rather than in separate categories.

Setting an LLM's temperature to zero should make its output deterministic, but it doesn't in practice. This is because floating-point number additions, when parallelized across GPUs, are non-associative. The order in which batched operations complete creates tiny variations, preventing true determinism.