Real-time speech interaction, serving as a fundamental interface for human-machine collaboration, holds immense potential. However, current open-source models face limitations such as high costs in voice data collection, weakness in dynamic control, and limited intelligence. To address these challenges, this paper introduces Step-Audio, the first production-ready open-source solution. Key contributions include: 1) a 130B-parameter unified speech-text multi-modal model that achieves unified understanding and generation, with the Step-Audio-Chat version open-sourced; 2) a generative speech data engine that establishes an affordable voice cloning framework and produces the open-sourced lightweight Step-Audio-TTS-3B model through distillation; 3) an instruction-driven fine control system enabling dynamic adjustments across dialects, emotions, singing, and RAP; 4) an enhanced cognitive architecture augmented with tool calling and role-playing abilities to manage complex tasks effectively. Based on our new StepEval-Audio-360 evaluation benchmark, Step-Audio achieves state-of-the-art performance in human evaluations, especially in terms of instruction following. On open-source benchmarks like LLaMA Question, shows 9.3% average performance improvement, demonstrating our commitment to advancing the development of open-source multi-modal language technologies. Our code and models are available at https://github.com/stepfun-ai/Step-Audio.

Deep Reinforcement Learning combined with Fictitious Play shows impressive results on many benchmark games, most of which are, however, single-stage. In contrast, real-world decision making problems may consist of multiple stages, where the observation spaces and the action spaces can be completely different across stages. We study a two-stage strategy card game Legends of Code and Magic and propose an end-to-end policy to address the difficulties that arise in multi-stage game. We also propose an optimistic smooth fictitious play algorithm to find the Nash Equilibrium for the two-player game. Our approach wins double championships of COG2022 competition. Extensive studies verify and show the advancement of our approach.

Policy-based reinforcement learning methods suffer from the policy collapse problem. We find valued-based reinforcement learning methods with {\epsilon}-greedy mechanism are capable of enjoying three characteristics, Closed-form Diversity, Objective-invariant Exploration and Adaptive Trade-off, which help value-based methods avoid the policy collapse problem. However, there does not exist a parallel mechanism for policy-based methods that achieves all three characteristics. In this paper, we propose an entropy regularization free mechanism that is designed for policy-based methods, which achieves Closed-form Diversity, Objective-invariant Exploration and Adaptive Trade-off. Our experiments show that our mechanism is super sample-efficient for policy-based methods and boosts a policy-based baseline to a new State-Of-The-Art on Arcade Learning Environment.

Building on the breakthrough of reinforcement learning, this paper introduces a unified framework of model-free reinforcement learning, CASA-B, Critic AS an Actor with Bandits Vote Algorithm. CASA-B is an actor-critic framework that estimates state-value, state-action-value and policy. An expectation-correct Doubly Robust Trace is introduced to learn state-value and state-action-value, whose convergence properties are guaranteed. We prove that CASA-B integrates a consistent path for the policy evaluation and the policy improvement. The policy evaluation is equivalent to a compensational policy improvement, which alleviates the function approximation error, and is also equivalent to an entropy-regularized policy improvement, which prevents the policy from collapsing to a suboptimal solution. Building on this design, we find the entropy of the behavior policies' and the target policy's are disentangled. Based on this observation, we propose a progressive closed-form entropy control mechanism, which explicitly controls the behavior policies' entropy to arbitrary range. Our experiments show that CASAB is super sample efficient and achieves State-Of-The-Art on Arcade Learning Environment. Our mean Human Normalized Score is 6456.63% and our median Human Normalized Score is 477.17%, under 200M training scale.