Amazon launches reinforcement fine‑tuning for Nova, teaching AI via feedback

Amazon’s reinforcement fine‑tuning (RFT) for Nova is a shift from the classic supervised‑learning pipeline to a feedback‑driven loop that evaluates outputs against explicit reward criteria rather than mimicking a fixed set of demonstrations. In the AWS blog post, Amazon describes RFT as “learning by evaluation rather than imitation,” allowing developers to supply only prompts and a definition of correctness—such as test cases, verifiable outcomes, or quality thresholds—while the model iteratively refines its policy to maximize those signals (AWS). This eliminates the need for thousands of hand‑crafted, step‑by‑step examples that traditional fine‑tuning demands, a pain point the post notes is “expensive, time‑consuming” for many real‑world tasks where multiple solution paths exist.

The new capability is tightly coupled with the Nova 2 family, launched in December 2025, which already embeds a reasoning engine that decomposes problems into intermediate steps before producing a final answer (AWS). When RFT is applied to these reasoning models, the feedback loop can target not only the correctness of the final output but also the efficiency of the reasoning process itself. Amazon claims this can “reduce token usage” by teaching the model to discover more concise reasoning paths, a benefit that directly translates into lower inference costs for large‑scale deployments.

From an implementation standpoint, Amazon offers three tiers of access. The simplest entry point is a fully managed service in Amazon Bedrock, where users can enable RFT with a few clicks and let the platform handle data ingestion, reward‑function definition, and iterative training (AWS). For teams that need tighter integration with custom pipelines, SageMaker Training Jobs expose the same RFT primitives while allowing bespoke preprocessing and logging. The most demanding workloads can leverage SageMaker HyperPod for distributed training at petabyte scale, or Nova Forge for multi‑turn agentic workflows that simulate reinforcement‑learning environments (AWS). All current RFT support is limited to text‑only use cases, but the architecture is designed to accommodate future multimodal extensions.

Practical guidance in the blog post emphasizes three pillars: data preparation, reward‑function design, and best‑practice tuning. Data preparation involves curating a set of prompts that represent the target domain and defining deterministic evaluation scripts—e.g., unit tests for code generation or numeric tolerance checks for math reasoning. Reward functions must be carefully calibrated to avoid “reward hacking,” where the model learns to game the metric without delivering genuine value. Amazon recommends starting with binary success/failure signals and gradually introducing graded rewards as the model stabilizes (AWS). The post also notes that RFT shines in domains such as code generation, where outputs can be automatically compiled and tested, and customer‑service automation, where compliance and tone can be encoded as rule‑based validators.

Analysts have pointed out that Amazon’s RFT rollout arrives at a moment when the industry is grappling with the scalability of supervised fine‑tuning. OpenAI, for example, continues to rely on large labeled datasets for its instruction‑tuned models, a process that has prompted Amazon to invest heavily in its own foundation‑model stack (The Information). By offering a feedback‑centric alternative, Amazon positions Nova as a more adaptable platform for enterprises that lack the resources to produce exhaustive annotation pipelines. The move also dovetails with Amazon’s broader AI strategy, which includes a potential $50 billion investment in OpenAI contingent on milestones such as an IPO or AGI breakthrough (The Information). While the financial tie‑in is unrelated to the technical rollout, it underscores Amazon’s ambition to dominate the enterprise AI stack, and RFT could be a differentiator in that race.

In summary, Amazon’s reinforcement fine‑tuning for Nova introduces a practical, evaluation‑driven customization path that reduces the annotation burden, optimizes reasoning efficiency, and integrates across the full spectrum of AWS AI services. The approach is currently limited to text‑only scenarios, but its modular design—spanning Bedrock, SageMaker, HyperPod, and Nova Forge—offers a clear migration path for organizations seeking to scale from prototype to production while retaining fine‑grained control over model behavior.