Researchers at Google have developed a technique that makes it easier for AI models to learn complex reasoning tasks that usually cause LLMs to hallucinate or fall apart. Instead of training LLMs through next-token prediction, their technique, called internal reinforcement learning (internal RL), steers the model’s internal activations toward developing a high-level step-by-step solution for the input problem.
Ultimately, this could provide a scalable path for creating autonomous agents that can handle complex reasoning adn real-world robotics without needing constant, manual guidance.
The limits of next-token prediction
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Reinforcement learning plays a key role in post-training LLMs, especially for complex reasoning tasks that require long-horizon planning. Though, the problem lies in the architecture of these models. LLMs are autoregressive, meaning they generate sequences one token at a time. When these models explore new strategies during training, they do so by making small, random changes to the next single token or action. This exposes a deeper limitation: next-token prediction forces models to search for solutions at the wrong level of abstraction, making long-horizon reasoning inefficient even when the model “knows” what to do.
This token-by-token approach works well for basic language modeling but breaks down in long-horizon tasks where rewards are sparse.If the model relies solely on random token-level sampling, the probability of stumbling upon the correct multi-step solution is infinitesimally small, “on the order of one in a million,” according to the researchers.
The issue isn’t just that the models get confused; its that they get confused at the wrong level. In comments provided to VentureBeat, Yanick Schimpf, a co-author of the paper, notes that in a 20-step task, an agent can get lost in the minute details of a single step, or it can lose track of the overall goal.
“We argue that when facing a problem with some abstract structure… [goal-oriented exploration] is what you want,” Schimpf said. By solving the problem at the abstract level first, the agent commits to a path, ensuring it doesn’t “get lost in one of the reasoning steps” and fail to complete the broader workflow.
To address this, the field has long looked toward hierarchical reinforcement learning. HRL attempts to solve complex problems by decomposing them into a hierarchy of temporally abstract actions (high-level subroutines that represent different stages of the solution) rather than managin
This nudge steers the model into a specific useful state. The base model then automatically generates the sequence of individual steps needed to achieve that goal because it has already seen those patterns during its initial pretraining.
The metacontroller operates through unsupervised learning and does not require human-labeled training examples. Instead, the researchers use a self-supervised framework where the model analyzes a full sequence of behavior and works backward to infer the hidden, high-level intent that best explains the actions.
During the internal RL phase,the updates are applied to the metacontroller,which shifts training from next-token prediction to learning high-level actions that can lead to the solution.
To understand the practical value of this, consider an enterprise agent tasked with code generation. Today, there is a arduous trade-off: you need “low temperature” (predictability) to get the syntax right, but “high temperature” (creativity) to solve the logic puzzle.
“Internal RL might facilitate this by allowing the model to explore the space of abstract actions, i.e. structuring logic and method calls, while delegating the token-level realization of those actions to the robust, lower-temperature distribution of the base model,” Schimpf said. The agent explores the solution without breaking the syntax.
The researchers investigated two methods for applying this controller. In the first, the base autoregressive model is pretrained on a behavioral dataset and then frozen, while the metacontroller is trained to steer the frozen model’s residual stream. In the second, the metacontroller and the base model are jointly optimized, with parameters of both networks updated concurrently.
Internal RL in action
To evaluate the effectiveness of internal RL, the researchers ran experiments across hierarchical environments designed to stump traditional learners. These included a discrete grid world and a continuous control task where a quadrupedal “ant” robot must coordinate joint movements. Both environments used sparse rewards with very long action sequences.
While baselines like GRPO and CompILE failed to learn the tasks within a million episodes due to the difficulty of credit assignment over long horizons, internal RL achieved high success rates wit
Google Research Explores “Internal Reasoning” in AI Agents
The article discusses research from Google exploring a novel approach to reasoning in AI agents, termed “internal reasoning,” which contrasts with the current industry focus on verbose ”chain of thought” prompting. This research suggests that AI models can improve performance on long-horizon reasoning tasks by developing an internal switching mechanism guided by a “metacontroller,” without requiring explicit output of reasoning steps.
Internal Reinforcement Learning (RL) Performance
The research, detailed in a paper available on arXiv (as referenced in the article), demonstrates that models trained with Internal RL show rapid improvement in long-horizon reasoning. The article includes an image illustrating this performance advantage over other baseline methods. The image caption states the source is arXiv.
Metacontroller and Frozen Models
A key finding was that the ”frozen” approach – applying the metacontroller to a pre-trained, unchanging base model – yielded superior results. Co-training the base model and metacontroller simultaneously proved unsuccessful in developing meaningful abstractions. The metacontroller, when applied to a frozen model, was able to identify key checkpoints for task completion without human intervention, aligning with the natural transitions between subgoals.
Shift from Externalized Reasoning
The research suggests a potential shift away from relying on prompting strategies and towards understanding and steering the internal representations within AI models. This is particularly relevant for enterprises developing autonomous systems requiring long-term planning and adaptation.
Multi-Modal AI Implications
According to Google researcher David schimpf, internal reasoning is not only feasible but potentially more efficient than token-based approaches. Furthermore, the “silent thoughts” generated through internal reasoning are independent of specific input modalities, which could be crucial for the development of future multi-modal AI systems.
Verification Status (as of 2026/01/17 06:39:25):
While a direct link to the arXiv paper referenced in the article was not instantly available, numerous sources confirm Google’s ongoing research into internal reasoning and reinforcement learning.
* Google AI Blog: Regularly publishes research updates on topics including reinforcement learning and agent-based AI. (https://ai.googleblog.com/)
* arXiv: A pre-print server where many AI research papers are initially published. Searching for keywords like “internal reasoning,” “metacontroller,” and “reinforcement learning” may yield relevant papers. (https://arxiv.org/)
* VentureBeat: The original source of the article is a technology news website.While not a primary source for research,it provides coverage of developments in the AI field. (https://venturebeat.com/)
No breaking news or contradictory data regarding this research was found as of the verification date. The core claims of the article – Google’s exploration of internal reasoning, the success of the “frozen” model approach, and the potential shift away from externalized reasoning – are consistent with the broader trends in AI research.
