Large language models achieve strong reasoning but require substantial parameters and memory, limiting deployment in resource-constrained settings. We introduce the Tiny Recursive Language Model (TRLM), a small masked non-autoregressive model that increases effective computation depth through shared-weight recursion. TRLM represents a masked sequence with input, answer, and latent reasoning states. A shared Transformer repeatedly refines the latent reasoning state, conditioning each update on trajectory time and step size via adaptive layer normalization. Training combines long- and short-trajectory answer-prediction losses with representation-level consistency. At inference, TRLM recursively refines latent states and fills all masked answer tokens in a single parallel decoding pass. We pretrain TRLM on approximately one billion OpenWebText and FineWeb-Edu tokens, then fine-tune and evaluate it on GSM8K and SVAMP against scratch-pretrained GPT-2-style, Gemma-4-Text-style, Diffusion-Gemma-style, and Mamba-2-style baselines. TRLM matches the best observed GSM8K result with 2.88% exact-match accuracy and achieves the highest SVAMP point estimate in our controlled comparison at 9.00%. Performance is non-monotonic with recursion depth: the highest observed GSM8K accuracy occurs at eight steps, whereas SVAMP peaks at four, suggesting that the preferred recursion depth may depend on the task in this setting.