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Researchers from Together AI and Agentica have introduced DeepCoder-14B, an innovative coding model that showcases performance levels on par with leading proprietary models such as OpenAI’s o3-mini.
DeepCoder-14B builds upon the foundation set by DeepSeek-R1, offering enhanced versatility in integrating advanced code generation and reasoning capabilities into practical applications. Notably, the model, along with its training dataset, code, logs, and optimization strategies, has been completely open-sourced, fostering opportunities for the research community to enhance their work and expedite advancements in the field.
Competitive Coding Features in a Compact Model
The research team conducted extensive experiments demonstrating DeepCoder-14B’s robust performance across various challenging coding benchmarks such as LiveCodeBench (LCB), Codeforces, and HumanEval+.
“In all coding benchmarks, our model exhibits strong performance… comparable to the results of o3-mini (low) and o1,” the researchers detailed in a blog post regarding the model’s capabilities.
Remarkably, although primarily trained on coding tasks, the model has also shown enhanced mathematical reasoning, achieving a score of 73.8% on the AIME 2024 benchmark. This marks a notable 4.1% improvement from its predecessor, DeepSeek-R1-Distill-Qwen-14B, indicating that the reasoning abilities developed through reinforcement learning on code can be effectively applied to broader domains.
Credit: Together AI
A standout feature of DeepCoder-14B is its capability to deliver such high performance with only 14 billion parameters. This characteristic not only makes it more compact but also potentially increases efficiency compared to many larger models in the field.
Innovative Elements Enhancing DeepCoder’s Efficiency
During the model’s development, the research team tackled several significant challenges inherent in training coding models using reinforcement learning (RL).
One of the primary challenges involved curating training data, as effective RL relies on precise reward signals that indicate the correctness of the model’s outputs. The researchers noted that, unlike other fields such as mathematics—where there is an abundance of verified data available online—the coding domain lacks such richness.
To combat this issue, the DeepCoder team established a rigorous pipeline to collect examples from various datasets, filtering for validity, complexity, and avoidance of duplication. This meticulous approach resulted in 24,000 high-quality problems, forming a strong foundation for effective RL training.
Additionally, the team devised a straightforward reward function that only provides positive feedback if the generated code successfully passes all relevant unit tests within a set time frame. Coupled with high-quality training examples, this outcome-driven reward structure prevents the model from resorting to tricks like memorizing answers or solving overly simplified edge cases without addressing the primary problem.
The model’s core training algorithm utilizes Group Relative Policy Optimization (GRPO), an approach that has shown great success in DeepSeek-R1. However, the team implemented several modifications to enhance the algorithm’s stability, allowing for extended training periods without diminishing returns.
GRPO+ allows DeepCoder-14 to sustain performance over prolonged training sessions. Credit: Together AI
Moreover, the researchers gradually expanded the model’s context window, starting with shorter reasoning sequences and progressively increasing the complexity. They also developed filtering techniques to prevent penalizing the model for generating longer reasoning chains that exceed the context limits when addressing challenging prompts.
DeepCoder was trained using contexts of 32K but demonstrated the ability to solve problems requiring up to 64K tokens. Credit: Together AI
The team articulated their strategy: “To maintain long-context reasoning while ensuring effective training, we introduced overlong filtering… This approach masks truncated sequences during training, allowing models to generate thoughtful yet lengthy outputs without penalties.”
The training gradually progressed from a context window of 16K to 32K, enabling the resultant model to tackle problems that necessitate up to 64K tokens.
Enhancing Long-Context Reinforcement Learning Training
Training large models through RL, particularly for tasks involving extensive output sequences like coding or complex reasoning, is resource-intensive and time-consuming. A significant hurdle arises during the “sampling” phase, where the model generates a potentially vast number of tokens per example in a batch. Fluctuations in response lengths can lead to some responses completing significantly later than others, causing GPU resources to remain idle and decelerating the overall training process.
To expedite this process, the team introduced verl-pipeline, an optimized extension of the open-source verl library focused on reinforcement learning from human feedback (RLHF). A key innovation, termed “One-Off Pipelining,” reorganizes the sampling and model update processes to minimize bottlenecks and idle times of accelerators.
One-Off Pipelining
Initial experiments revealed that one-off pipelining could achieve up to a 2x speed increase for coding RL tasks in comparison to baseline methods. This was crucial for training DeepCoder effectively within a manageable timeframe (2.5 weeks utilizing 32 H100s) and is now available as an open-source component of verl-pipeline for others in the community to utilize and enhance.
Impact on Enterprises
The researchers have made all components necessary for training and deploying DeepCoder-14B accessible on GitHub and Hugging Face, distributed under a permissive license.
“By openly sharing our dataset, code, and training methodology, we enable the community to replicate our findings and make RL training more accessible,” the researchers noted.
DeepCoder-14B exemplifies a significant and growing trend within the AI landscape: the emergence of powerful, efficient, and openly available models. This shift represents a pivotal change for the enterprise sector, presenting numerous opportunities for enhanced access to advanced AI technologies.
With sophisticated models like DeepCoder becoming available, organizations of all sizes can harness the power of advanced code generation and reasoning capabilities, customizing solutions to fit their specific requirements while ensuring secure deployment in their own infrastructures. This evolution can lower the barriers to AI adoption and stimulate a more dynamic and innovative ecosystem, driven by collective open-source collaboration.
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