Exploring Reinforcement Learning in Context in LLM with Sparse Autoencoders

Large language models (LLMs) have demonstrated remarkable in-context learning capabilities in several domains, including translation, functional learning, and reinforcement learning. However, the underlying mechanisms of these abilities, particularly in reinforcement learning (RL), remain poorly understood. Researchers are trying to unravel how LLMs learn to generate actions that maximize discounted future rewards through trial and error, given only a scalar reward signal. The central challenge lies in understanding how LLMs implement temporal difference (TD) learning, a fundamental concept in RL that involves updating value beliefs based on the difference between expected and actual rewards.

Previous research has explored learning in context from a mechanistic perspective, demonstrating that transformers can discover existing algorithms without explicit guidance. Studies have shown that transformers can implement various reinforcement and regression learning methods in context. Sparse autoencoders have been successfully used to decompose language model activations into interpretable features, identifying both concrete and abstract concepts. Several studies have investigated the integration of reinforcement learning and language models to improve performance on various tasks. This research contributes to the field by focusing on understanding the mechanisms through which large linguistic models implement reinforcement learning, building on the existing literature on learning in context and the interpretability of the models.

Researchers at the Institute for Human-Centered ai, the Helmholtz Center for Computational Health, and the Max Planck Institute for Biological Cybernetics have employed sparse autoencoders (SAEs) to analyze the representations that support in-context learning in virtual reality environments. This approach has proven successful in building a mechanistic understanding of neural networks and their representations. Previous studies have applied SAE to various aspects of neural network analysis, demonstrating its effectiveness in uncovering underlying mechanisms. By using SAE to study RL in context in Llama 3 70B, the researchers aim to systematically investigate and manipulate the model's learning processes. This method allows identifying representations similar to TD errors and Q values ​​in multiple tasks, providing insights into how LLMs implement RL algorithms through next token prediction.

The researchers developed a methodology to analyze reinforcement learning in context in Llama 3 70B using SAE. They designed a simple Markov decision process inspired by the two-step task, where Llama had to make sequential decisions to maximize rewards. Model performance was evaluated through 100 independent experiments, each consisting of 30 episodes. SAEs were trained on the residual flux outputs of the Llama transformer blocks, using variations of the two-step task to create a diverse training set. This approach allowed researchers to discover similar representations of TD errors and Q values, providing insights into how Llama implements RL algorithms through next token prediction.

The researchers extended their analysis to a more complex 5×5 grid navigation task, where Llama predicted the actions of the Q-learning agents. They found that Llama improved their action predictions over time, especially when given correct reward information. SAEs trained on Llama residual flow representations revealed latents highly correlated with the Q values ​​and TD errors of the generating agent. Disabling or blocking these TD latents significantly degraded Llama's action prediction ability and reduced correlations with Q values ​​and TD errors. These findings further support the hypothesis that Llama's internal representations encode reinforcement learning-like computations, even in more complex environments with larger state and action spaces.

The researchers are investigating Llama's ability to learn graph structures without rewards, using a concept called Successor Representation (SR). They asked Llama for observations from a random walk on a latent community graph. The results showed that Llama quickly learned to predict the next state with high accuracy and developed SR-like representations, capturing the global geometry of the graph. Sparse autoencoder analysis revealed stronger correlations with SR and associated TD errors than with model-based alternatives. Deactivating key TD latents affected Llama's prediction accuracy and disrupted its learned graphical representations, demonstrating the causal role of TD-like computations in Llama's ability to learn structural knowledge.

This study provides evidence that large language models (LLMs) implement temporal difference learning (TD) to solve reinforcement learning problems in context. Using sparse autoencoders, researchers identified and manipulated features crucial to learning in context, demonstrating their impact on LLM behavior and representations. This approach opens avenues to study various learning abilities in context and establishes a connection between LLM learning mechanisms and those observed in biological agents, which implement TD computations in similar scenarios.

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Asjad is an internal consultant at Marktechpost. He is pursuing B.tech in Mechanical Engineering from Indian Institute of technology, Kharagpur. Asjad is a machine learning and deep learning enthusiast who is always researching applications of machine learning in healthcare.