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The second half of 2025 saw a significant resurgence of interest in fine-tuning among major tech companies. This shift is not accidental but a structural change driven by breakthroughs in reinforcement learning algorithms, decreasing costs of very large models, and innovations in training paradigms.

The New Paradigm of Reinforcement Fine-Tuning

The phrase “Reinforcement fine-tuning, build Agents” pinpoints the core evolution in fine-tuning technology. Traditional Supervised Fine-Tuning (SFT) is like making students memorize answers, whereas current fine-tuning focuses more on cultivating the model’s “thinking ability” to solve problems.

GRPO: Rethinking RL Alignment The GRPO algorithm, proposed by DeepSeek in DeepSeek Math, replaces the absolute value estimation in traditional PPO with group-relative advantage estimation, eliminating the need for a massive Critic network. This design significantly reduces training complexity. In verifiable scenarios like mathematical reasoning, it drives model evolution using sparse but accurate reward signals (e.g., answer correctness).

The Training Shift from “Imitation” to “Thinking” The “Mid-training” paradigm proposed by Meta is a typical example. It enables AI agents to learn through “early experiences” — trying actions within an environment and observing the resulting state changes (e.g., clicking a wrong button triggers an error message). This approach allows the model not only to learn “how” to act but also to understand “why,” laying a solid foundation for developing into generalist agents capable of handling complex situations.

A More Efficient “Master-Apprentice” Mechanism Online Policy Distillation, proposed by Thinking Machines Lab, offers an efficient RL training mode. In this method, a smaller model (the student) attempts to solve problems independently, while a larger, more powerful model (the teacher) provides detailed step-by-step evaluation and guidance. This approach combines the benefits of RL and self-exploration while mitigating the issue of sparse feedback, leading to substantial gains in training efficiency.

Precise Control via Process Reward The L2T framework from the Institute of Software, Chinese Academy of Sciences, further enriches this concept. It introduces an information-theory-based dense process-level reward mechanism that incentivizes logical reasoning steps and suppresses redundant generation by evaluating the information gain at each reasoning step. This contrasts sharply with rewards based solely on the final outcome.

Co-evolution of Model Scale and Architecture

Models like Kimi and GLM are pushing into the 100B-1T parameter range, continuously raising the upper limit of model scale, while the underlying technical rationale is also evolving.

Very Large Models “Become More Accessible”: As seen with trillion-parameter models like Kimi K2 (1T) and DeepSeek (671B), their massive computational demands were once prohibitive. Now, methods like LoRA and the DeepSpeed parallel algorithm significantly reduce development costs and time by optimizing GPU memory usage.

Architectural Innovation for “One Fine-Tuning, Universal Expertise”: The SIMoE framework, proposed by institutions like Zhejiang University, offers a novel approach. With a single round of instruction fine-tuning, it can upgrade a standard dense model into an intrinsic “team of experts.” Different internal “experts” within this model can collaborate dynamically, invoking the most suitable capabilities for different tasks, thereby achieving breakthroughs in both performance and efficiency across multiple benchmarks.

The New Frontier of Self-Evolution: MIT’s SEAL framework demonstrates a model’s ability to autonomously generate fine-tuning data and self-update instructions. This nested, two-level learning mechanism allows the model to compute rewards based on its task performance, further optimizing the strategy for generating self-update instructions, pointing towards future directions for continuous model self-improvement.

Fine-Tuning vs. Context Engineering: Why the Shift Now?

The Fundamental Limitations of In-Context Learning Providing context to models through prompts is indeed simple and fast, suitable for lightweight task adaptation. However, it carries risks like brevity bias (potentially omitting critical details) and context collapse (degrading information utility after repeated modifications). More importantly, knowledge acquired this way resembles “temporary memory” rather than being truly internalized into the model’s capabilities.

The Deep Reshaping Value of Fine-Tuning In principle, Reinforcement Learning Fine-Tuning essentially deeply reshapes the model’s decision boundaries through reward signals. Research from Carnegie Mellon University and Cornell University theoretically explains that when a “generate-verify” gap exists in a task, RL exhibits its advantage by filtering the policy space, restricting the search to policies that are optimal for the verifier.

Future Outlook: Towards a Three-Stage Training Paradigm

Synthesizing cutting-edge research, a future development path is becoming clear:

The Three-Stage Evolution of Training Paradigms The “Pre-training — Mid-training — Post-training” paradigm proposed by Meta is emerging as a new standard. The Mid-training phase, where agents accumulate “early experiences” to understand the causality between actions and environmental changes, lays a crucial foundation for subsequent reinforcement learning.

The Inevitable Trend of Technological Fusion We are witnessing the deep integration of Reinforcement Learning, Parameter-Efficient Fine-Tuning, and In-Context Engineering. They are no longer mutually exclusive choices but work synergistically across different stages of the model’s lifecycle, depending on task requirements.

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